Overview
First we’ll look at the uncorrected data; we’ll just correct for mitochondrial percentage and nothing else. Then we’ll run PCA, clustering and look at the UMAP plots to get an idea of how much run-to-run and technology variation we are dealing with.
seurat = SCTransform(seurat, vars.to.regress = "pctMito", verbose = FALSE)
seurat = RunPCA(seurat, verbose = FALSE)
seurat = RunUMAP(seurat, dims = 1:30, verbose = FALSE)
p1 = DimPlot(seurat, group.by = "technology")
p1

A lot! We can see that the 10x and the inDrop data are mostly separate from each other, but there is some overlaps. We can see looking at the four samples that the four samples are very different from each other as well.
DimPlot(seurat, group.by = "sample")

So we are going to have some work to do trying to overlay these on top of each other. There are a few ways to do that. One is to just regress out the effect of the technology, the other is to do something more complex like CCA. We do need to do something about it though if we want to combine these two datasets. However, even without doing anything about the technological variation we can take a peek at the data and see if we can pick out some patterns.
uncorrected clustering
Here we’ll do a rough clustering and then look at the expression of our markers, without correcting for which technology or experiment they are from, to orient outselves.
clustering
ElbowPlot(seurat, ndims=50)

seurat = FindNeighbors(seurat, dims=1:25)
seurat = FindClusters(seurat, resolution=0.5)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 10605
## Number of edges: 332518
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9401
## Number of communities: 25
## Elapsed time: 0 seconds
DimPlot(seurat)

cell type markers
markers = readr::read_csv("metadata/cell_type_markers.csv")
knitr::kable(markers)
| ISC |
Dl |
| ISC |
Smvt |
| ISC |
CG10006 |
| pISC |
sna |
| ISCR2R5 |
polo |
| ISC/EB |
esg |
| EB |
E(spl)m3-HLH |
| EB |
E(spl)malpha-BFM |
| EB |
E(spl)mbeta-HLH |
| EB |
klu |
| EE |
pros |
| EE |
7B2 |
| EE_subtype |
AstA |
| EE_subtype |
AstC |
| EE_subtype |
CCHa1 |
| EE_subtype |
CCHa2 |
| EE_subtype |
Tk |
| EE_subtype |
NPF |
| EE_subtype |
Orcokinin |
| EE_subtype |
Dh31 |
| aEC |
nub |
| aEC |
Myo31DF |
| mEC |
nub |
| mEC |
Myo31DF |
| pEC |
nub |
| pEC |
Myo31DF |
| aEC |
betaTry |
| pEC |
lambdaTry |
| mEC |
lab |
| mEC |
Vha100-4 |
| cardia |
Pgant4 |
| copper |
PGRP-LA |
| copper |
PGRP-LC |
| copper |
Apoltp |
| VM |
Vn |
| LFC |
Ilp3 |
| LFC |
PGRP-SC1a |
| LFC |
PGRP-SC1b |
| LFC |
PGRP-SD |
| iron |
PGRP-SC2 |
| iron |
ZIP1(Zip42C.1) |
Below we can see that cluster 3 and cluster 21 are likely ISC+EB or EB cells. Cluster 9, 17 and 20 are liekly some subtypes of EE cells. Groups 1, 4, 11, 14 and 23 might be aEC cells. Groups 5, 13 and 15 might be pEC cells. Group 8 looks like mECs. And group 15 looks like cardia cells.
Cluster 12 looks like it might be LFC cells. Cluster 7 might be iron or copper cells. Cluster 6 looks like it could be an EC cell, but we don’t know what compartment.
We’re just missing 2, 10, 18, 19, and 22. Not bad.
cluster.averages = AverageExpression(seurat, return.seurat=TRUE, add.ident="sample")
DoHeatmap(cluster.averages, features=head(markers, 20)$genes, size=2)

DoHeatmap(cluster.averages, features=tail(markers, 21)$genes, size=2)

clusters = seurat@meta.data$seurat_clusters
rough_celltype = case_when(
clusters %in% c(3, 21) ~ "ISC/EB",
clusters %in% c(9, 17, 20) ~ "EE",
clusters %in% c(0, 1, 4, 11, 14, 23) ~ "aEC",
clusters %in% c(5, 13, 16) ~ "pEC",
clusters %in% c(8, 24) ~ "mEC",
clusters %in% c(6) ~ "EC",
clusters %in% c(15) ~ "cardia",
clusters %in% c(12) ~ "LFC",
clusters %in% c(7) ~ "iron/copper",
TRUE ~ as.character(clusters))
seurat@meta.data$rough_celltype = rough_celltype
Idents(seurat) = seurat@meta.data$rough_celltype
roughcelltype.averages = AverageExpression(seurat, return.seurat=TRUE, add.ident="sample")
DoHeatmap(roughcelltype.averages, features=head(markers, 20)$genes, size=2)

DoHeatmap(roughcelltype.averages, features=tail(markers, 21)$genes, size=2)

p3 = DimPlot(seurat, group.by="rough_celltype", label=TRUE)
p3

It looks like we are able to identify the cells across both technologies, which is very good.
knitr::kable(seurat@meta.data %>%
group_by(rough_celltype, technology) %>%
summarise(n=n()))
| 10 |
10x |
76 |
| 10 |
inDrop |
323 |
| 18 |
10x |
31 |
| 18 |
inDrop |
214 |
| 19 |
inDrop |
243 |
| 2 |
10x |
100 |
| 2 |
inDrop |
683 |
| 22 |
10x |
10 |
| 22 |
inDrop |
77 |
| aEC |
10x |
913 |
| aEC |
inDrop |
3142 |
| cardia |
10x |
32 |
| cardia |
inDrop |
225 |
| EC |
10x |
186 |
| EC |
inDrop |
316 |
| EE |
10x |
306 |
| EE |
inDrop |
551 |
| iron/copper |
10x |
206 |
| iron/copper |
inDrop |
266 |
| ISC/EB |
10x |
141 |
| ISC/EB |
inDrop |
643 |
| LFC |
10x |
102 |
| LFC |
inDrop |
221 |
| mEC |
10x |
252 |
| mEC |
inDrop |
226 |
| pEC |
10x |
624 |
| pEC |
inDrop |
496 |
unmarked clusters
This leaves us with clusters 2, 10, 18, 19 and 22 as unknown clusters.
seurat@meta.data %>%
filter(is.na(rough_celltype)) %>%
pull(seurat_clusters) %>%
unique()
## factor(0)
## 25 Levels: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 ... 24
trypsin genes
tryp = c("deltaTry", "gammaTry", "alphaTry", "betaTry",
"epsilonTry", "thetaTry", "kappaTry", "lambdaTry",
"iotaTry", "etaTry", "zetaTry")
r1 = c("deltaTry", "gammaTry", "Muc68D")
r2 = c("deltaTry", "gammaTry", "alphaTry", "betaTry", "epsilonTry", "thetaTry")
r3 = c("thetaTry")
r4 = c("kappaTry", "lambdaTry", "iotaTry", "etaTry", "zetaTry")
r5 = c("iotaTry", "etaTry", "zetaTry")
crop = c("Cyp312a1", "Cyp4e3", "Spn27A", "spz", "pot")
compartments = unique(c(r1, r2, r3, r4, r5, crop))
FeaturePlot(seurat, features=r2)

FeaturePlot(seurat, features=r4)

FeaturePlot(seurat, features=r5)

FeaturePlot(seurat, features=crop)

DoHeatmap(cluster.averages, features=compartments, size=2)

It looks like group 12 and 15 might be from R1. Group 0, 1, 4, 11, 14, 19, 23 and 24 are from either R2 or R3. Group 7 looks like it might be from R3. Groups 5, 6 and 16 look like they are from R4. Group 18 looks like it is from the crop.
clusters = seurat@meta.data$seurat_clusters
compartment = case_when(
clusters %in% c(12, 15) ~ "R1",
clusters %in% c(0, 1, 4, 11, 14, 19, 23, 24) ~ "R2",
clusters %in% c(7) ~ "R3",
clusters %in% c(5, 6, 16) ~ "R4",
clusters %in% c(18) ~ "crop",
TRUE ~ as.character(clusters))
seurat@meta.data$compartment = compartment
Idents(seurat) = seurat@meta.data$compartment
compartment.averages = AverageExpression(seurat, return.seurat=TRUE, add.ident="sample")
DoHeatmap(compartment.averages, features=compartments, size=2)

p5 = DimPlot(seurat, group.by="compartment", label=TRUE)
p5

It looks like we are able to identify compartments cells are from across both technologies, which is very good.
knitr::kable(seurat@meta.data %>%
group_by(compartment, technology) %>%
summarise(n=n()))
| 10 |
10x |
76 |
| 10 |
inDrop |
323 |
| 13 |
10x |
161 |
| 13 |
inDrop |
113 |
| 17 |
10x |
165 |
| 17 |
inDrop |
85 |
| 2 |
10x |
100 |
| 2 |
inDrop |
683 |
| 20 |
10x |
35 |
| 20 |
inDrop |
138 |
| 21 |
10x |
140 |
| 21 |
inDrop |
4 |
| 22 |
10x |
10 |
| 22 |
inDrop |
77 |
| 3 |
10x |
1 |
| 3 |
inDrop |
639 |
| 8 |
10x |
252 |
| 8 |
inDrop |
208 |
| 9 |
10x |
106 |
| 9 |
inDrop |
328 |
| crop |
10x |
31 |
| crop |
inDrop |
214 |
| R1 |
10x |
134 |
| R1 |
inDrop |
446 |
| R2 |
10x |
913 |
| R2 |
inDrop |
3403 |
| R3 |
10x |
206 |
| R3 |
inDrop |
266 |
| R4 |
10x |
649 |
| R4 |
inDrop |
699 |
write seurat
saveRDS(seurat, file.path("results", "seurat-rough.rds"))
integrated clustering
Here we use the integrated data to re-do the clustering. After integration, we can see that the 10x and the inDrop datasets overlap with each other much better. We can also get a better idea about what the identities of cells might be.
integrated = ScaleData(integrated, verbose = FALSE)
integrated = RunPCA(integrated, npcs = 30, verbose = FALSE)
integrated = RunUMAP(integrated, reduction = "pca", dims = 1:30)
integrated = FindNeighbors(integrated, reduction = "pca", dims = 1:30)
integrated = FindClusters(integrated, resolution = 0.5)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 10605
## Number of edges: 407093
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9284
## Number of communities: 22
## Elapsed time: 0 seconds
p2 = DimPlot(integrated, reduction = "umap", group.by = "technology")
p4 = DimPlot(integrated, reduction = "umap", group.by = "rough_celltype", label=TRUE)
p6 = DimPlot(integrated, group.by="compartment", label=TRUE)
library(gridExtra)
grid.arrange(p1 + ggtitle("before integration"), p2 + ggtitle("after integration"))

grid.arrange(p3 + ggtitle("before integration"), p4 + ggtitle("after integration"))

grid.arrange(p5 + ggtitle("before integration"), p6 + ggtitle("after integration"))

Here we redo the marker expression, using the integrated cluster IDs rather than the unintegrated IDs from before.
DimPlot(integrated, group.by="seurat_clusters", label=TRUE)

integrated.averages = AverageExpression(integrated, return.seurat=TRUE, add.ident="sample")
DoHeatmap(integrated.averages, features=head(markers$genes, 20), size=2)

DoHeatmap(integrated.averages, features=tail(markers$genes, 21), size=2)

knitr::kable(markers)
| ISC |
Dl |
| ISC |
Smvt |
| ISC |
CG10006 |
| pISC |
sna |
| ISCR2R5 |
polo |
| ISC/EB |
esg |
| EB |
E(spl)m3-HLH |
| EB |
E(spl)malpha-BFM |
| EB |
E(spl)mbeta-HLH |
| EB |
klu |
| EE |
pros |
| EE |
7B2 |
| EE_subtype |
AstA |
| EE_subtype |
AstC |
| EE_subtype |
CCHa1 |
| EE_subtype |
CCHa2 |
| EE_subtype |
Tk |
| EE_subtype |
NPF |
| EE_subtype |
Orcokinin |
| EE_subtype |
Dh31 |
| aEC |
nub |
| aEC |
Myo31DF |
| mEC |
nub |
| mEC |
Myo31DF |
| pEC |
nub |
| pEC |
Myo31DF |
| aEC |
betaTry |
| pEC |
lambdaTry |
| mEC |
lab |
| mEC |
Vha100-4 |
| cardia |
Pgant4 |
| copper |
PGRP-LA |
| copper |
PGRP-LC |
| copper |
Apoltp |
| VM |
Vn |
| LFC |
Ilp3 |
| LFC |
PGRP-SC1a |
| LFC |
PGRP-SC1b |
| LFC |
PGRP-SD |
| iron |
PGRP-SC2 |
| iron |
ZIP1(Zip42C.1) |
Here we are missing classifying cluster 9, 13, and 16. classying 19 and 20 as ISC cells is pretty weak, IMO, so maybe those too.
clusters = integrated@meta.data$seurat_clusters
integrated_celltype = case_when(
clusters %in% c(3) ~ "ISC/EB",
clusters %in% c(12, 14, 15) ~ "EE",
clusters %in% c(1, 4, 18) ~ "aEC",
clusters %in% c(6) ~ "iron/copper",
clusters %in% c(5, 11, 13) ~ "pEC",
clusters %in% c(8) ~ "mEC",
clusters %in% c(7) ~ "LFC",
clusters %in% c(17) ~ "cardia",
TRUE ~ as.character(clusters))
integrated@meta.data$integrated_celltype = integrated_celltype
Idents(integrated) = integrated@meta.data$integrated_celltype
integratedcelltype.averages = AverageExpression(integrated, return.seurat=TRUE, add.ident="sample")
DoHeatmap(integratedcelltype.averages, features=markers$genes, size=2)

p7 = DimPlot(integrated, group.by="integrated_celltype", label=TRUE)
grid.arrange(p4 + ggtitle("integrated celltypes"), p7 + ggtitle("integrated celltypes"))

knitr::kable(integrated@meta.data %>%
dplyr::select(seurat_clusters, integrated_celltype) %>%
unique(), row.names=FALSE)
| 7 |
LFC |
| 5 |
pEC |
| 2 |
2 |
| 10 |
10 |
| 0 |
0 |
| 11 |
pEC |
| 6 |
iron/copper |
| 19 |
19 |
| 8 |
mEC |
| 21 |
21 |
| 1 |
aEC |
| 4 |
aEC |
| 13 |
pEC |
| 12 |
EE |
| 3 |
ISC/EB |
| 14 |
EE |
| 9 |
9 |
| 16 |
16 |
| 18 |
aEC |
| 20 |
20 |
| 15 |
EE |
| 17 |
cardia |
cluster markers (ROC)
Here we will find markers for each of the clusters where we haven’t been able to assign a cell type so we can hopefully figure out what type of cells they are.
Idents(integrated) = integrated@meta.data$seurat_cluster
Cluster 0
dir.create(file.path("results", "markers_roc"))
markersroc_0 = FindMarkers(integrated, ident.1=0, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markersroc_0, file.path("results", "markers_roc", "cluster0-markers_roc.csv"))
knitr::kable(head(markersroc_0, 25))
| CG12374 |
0.815 |
0.7426282 |
0.630 |
1.000 |
0.604 |
| Jon99Ciii |
0.790 |
0.3977139 |
0.580 |
0.999 |
0.669 |
| Bace |
0.777 |
0.4316666 |
0.554 |
0.996 |
0.599 |
| betaTry |
0.767 |
0.5517806 |
0.534 |
1.000 |
0.641 |
| Jon99Cii |
0.766 |
0.3550315 |
0.532 |
0.975 |
0.574 |
| alphaTry |
0.759 |
0.5690638 |
0.518 |
1.000 |
0.790 |
| Jon65Aiii |
0.745 |
0.2787393 |
0.490 |
0.975 |
0.627 |
| CG30025 |
0.743 |
0.4160439 |
0.486 |
0.973 |
0.601 |
| pre-rRNA:CR45856 |
0.257 |
-1.5008458 |
0.486 |
0.133 |
0.607 |
| Pebp1 |
0.734 |
0.3555240 |
0.468 |
0.876 |
0.524 |
| 18SrRNA:CR45841 |
0.273 |
-1.1015931 |
0.454 |
0.158 |
0.613 |
| 18SrRNA-Psi:CR45861 |
0.273 |
-1.5898494 |
0.454 |
0.091 |
0.528 |
| 18SrRNA:CR41548 |
0.277 |
-1.0260107 |
0.446 |
0.170 |
0.623 |
| 18SrRNA:CR45838 |
0.278 |
-1.0210926 |
0.444 |
0.171 |
0.623 |
| CG12057 |
0.721 |
0.6674180 |
0.442 |
0.624 |
0.298 |
| 18SrRNA-Psi:CR41602 |
0.279 |
-1.5761435 |
0.442 |
0.089 |
0.514 |
| pre-rRNA:CR45846 |
0.284 |
-0.9791249 |
0.432 |
0.243 |
0.699 |
| mt:CoI |
0.703 |
0.5018172 |
0.406 |
0.984 |
0.893 |
| mt:CoIII |
0.697 |
0.4695789 |
0.394 |
0.961 |
0.818 |
| His3.3B |
0.310 |
-0.4862776 |
0.380 |
0.268 |
0.682 |
| lncRNA:CR42491 |
0.311 |
-0.3863641 |
0.378 |
0.189 |
0.582 |
| 14-3-3epsilon |
0.316 |
-0.6482983 |
0.368 |
0.169 |
0.559 |
| crc |
0.317 |
-0.5028142 |
0.366 |
0.115 |
0.479 |
| asRNA:CR45874 |
0.322 |
-0.5536836 |
0.356 |
0.085 |
0.421 |
| Hr4 |
0.323 |
-0.5593047 |
0.354 |
0.142 |
0.504 |
Cluster 1
dir.create(file.path("results", "markers_roc"))
markers_roc1 = FindMarkers(integrated, ident.1=1, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc1, file.path("results", "markers_roc", "cluster1-markers_roc.csv"))
knitr::kable(head(markers_roc1, 25))
| CG6295 |
0.951 |
2.4180003 |
0.902 |
0.970 |
0.349 |
| CG12374 |
0.933 |
1.7278512 |
0.866 |
0.991 |
0.659 |
| betaTry |
0.932 |
1.4927853 |
0.864 |
0.992 |
0.691 |
| CG30025 |
0.923 |
1.5042412 |
0.846 |
0.992 |
0.650 |
| alphaTry |
0.919 |
1.2832729 |
0.838 |
0.992 |
0.819 |
| Jon99Ciii |
0.893 |
1.3578745 |
0.786 |
0.989 |
0.715 |
| Jon99Cii |
0.877 |
1.2946068 |
0.754 |
0.978 |
0.629 |
| CG30031 |
0.869 |
1.4176099 |
0.738 |
0.954 |
0.505 |
| gammaTry |
0.869 |
1.4176099 |
0.738 |
0.954 |
0.505 |
| deltaTry |
0.861 |
1.3654433 |
0.722 |
0.947 |
0.502 |
| CG5107 |
0.853 |
1.4549836 |
0.706 |
0.935 |
0.488 |
| epsilonTry |
0.846 |
1.4368083 |
0.692 |
0.934 |
0.433 |
| Jon65Aiii |
0.824 |
1.2721982 |
0.648 |
0.968 |
0.675 |
| Bace |
0.821 |
0.9766733 |
0.642 |
0.979 |
0.656 |
| Diedel3 |
0.809 |
1.2266437 |
0.618 |
0.879 |
0.467 |
| CG17192 |
0.808 |
2.2434487 |
0.616 |
0.692 |
0.104 |
| Jon99Ci |
0.800 |
1.3032560 |
0.600 |
0.822 |
0.325 |
| Amy-p |
0.797 |
0.8226741 |
0.594 |
0.848 |
0.395 |
| Jon25Bi |
0.796 |
2.4973360 |
0.592 |
0.712 |
0.171 |
| Jon65Aiv |
0.792 |
1.0718509 |
0.584 |
0.966 |
0.674 |
| tobi |
0.780 |
1.6524216 |
0.560 |
0.749 |
0.284 |
| Mal-A6 |
0.763 |
1.6997284 |
0.526 |
0.706 |
0.250 |
| Acbp5 |
0.242 |
-2.2991966 |
0.516 |
0.396 |
0.714 |
| rha |
0.747 |
1.2445401 |
0.494 |
0.634 |
0.189 |
| Mal-A1 |
0.743 |
0.9628729 |
0.486 |
0.827 |
0.463 |
Cluster 2
dir.create(file.path("results", "markers_roc"))
markers_roc2 = FindMarkers(integrated, ident.1=2, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc2, file.path("results", "markers_roc", "cluster2-markers_roc.csv"))
knitr::kable(head(markers_roc2, 25))
| CG13315 |
0.781 |
1.3570550 |
0.562 |
0.888 |
0.628 |
| pre-rRNA:CR45847 |
0.234 |
-1.6052927 |
0.532 |
0.435 |
0.771 |
| MtnC |
0.758 |
0.9374882 |
0.516 |
0.911 |
0.673 |
| pre-rRNA:CR45845 |
0.248 |
-1.3662956 |
0.504 |
0.546 |
0.838 |
| RpLP1 |
0.741 |
0.6907439 |
0.482 |
0.939 |
0.844 |
| RpLP2 |
0.741 |
0.5753640 |
0.482 |
0.988 |
0.948 |
| CG34330 |
0.738 |
0.8335108 |
0.476 |
0.868 |
0.654 |
| RpL41 |
0.738 |
0.6366086 |
0.476 |
0.990 |
0.972 |
| RpS26 |
0.734 |
0.6451404 |
0.468 |
0.942 |
0.857 |
| Cam |
0.730 |
0.9412521 |
0.460 |
0.872 |
0.708 |
| RpL37A |
0.728 |
0.6184733 |
0.456 |
0.965 |
0.914 |
| RpL35 |
0.727 |
0.6034513 |
0.454 |
0.965 |
0.889 |
| Acbp5 |
0.724 |
1.3897104 |
0.448 |
0.870 |
0.672 |
| RpL19 |
0.724 |
0.6111552 |
0.448 |
0.950 |
0.862 |
| RpS21 |
0.723 |
0.7083381 |
0.446 |
0.885 |
0.773 |
| RpS30 |
0.720 |
0.6082303 |
0.440 |
0.935 |
0.857 |
| CG43349 |
0.719 |
1.3817990 |
0.438 |
0.643 |
0.296 |
| RpL23 |
0.717 |
0.5709788 |
0.434 |
0.965 |
0.920 |
| sta |
0.715 |
0.5939545 |
0.430 |
0.949 |
0.879 |
| RpL15 |
0.712 |
0.6074820 |
0.424 |
0.948 |
0.853 |
| RpS27 |
0.711 |
0.5947775 |
0.422 |
0.954 |
0.885 |
| pre-rRNA:CR45846 |
0.290 |
-1.8422695 |
0.420 |
0.354 |
0.625 |
| RpS28b |
0.710 |
0.6617437 |
0.420 |
0.903 |
0.800 |
| RpL13 |
0.707 |
0.6758232 |
0.414 |
0.871 |
0.746 |
| RpL27A |
0.706 |
0.5697464 |
0.412 |
0.933 |
0.863 |
Cluster 3
markers_roc3 = FindMarkers(integrated, ident.1=3, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc3, file.path("results", "markers_roc", "cluster3-markers_roc.csv"))
knitr::kable(head(markers_roc3, 25))
| lncRNA:CR40469 |
0.971 |
2.3022701 |
0.942 |
0.999 |
0.907 |
| lncRNA:CR34335 |
0.969 |
2.0554202 |
0.938 |
0.996 |
0.804 |
| alphaTry |
0.114 |
-3.5902805 |
0.772 |
0.302 |
0.877 |
| Vha16-1 |
0.143 |
-2.2895249 |
0.714 |
0.243 |
0.837 |
| betaTry |
0.149 |
-3.7497180 |
0.702 |
0.116 |
0.765 |
| Jon65Aiv |
0.151 |
-3.2747248 |
0.698 |
0.089 |
0.748 |
| Jon99Ciii |
0.161 |
-3.7708719 |
0.678 |
0.230 |
0.779 |
| MtnC |
0.168 |
-2.7958055 |
0.664 |
0.108 |
0.738 |
| Bace |
0.169 |
-3.3875524 |
0.662 |
0.118 |
0.728 |
| CG30025 |
0.172 |
-3.6942286 |
0.656 |
0.121 |
0.724 |
| CG13315 |
0.185 |
-2.2055407 |
0.630 |
0.068 |
0.695 |
| Jon65Aiii |
0.185 |
-2.9903301 |
0.630 |
0.205 |
0.740 |
| CG12374 |
0.186 |
-3.4517539 |
0.628 |
0.180 |
0.727 |
| Acbp5 |
0.188 |
-2.5010183 |
0.624 |
0.159 |
0.730 |
| RpL41 |
0.808 |
0.5931470 |
0.616 |
0.992 |
0.972 |
| Vha13 |
0.195 |
-2.0011931 |
0.610 |
0.155 |
0.724 |
| MtnA |
0.201 |
-2.7763653 |
0.598 |
0.260 |
0.744 |
| CG34330 |
0.202 |
-1.9493609 |
0.596 |
0.176 |
0.711 |
| CG44008 |
0.202 |
-2.0279360 |
0.596 |
0.038 |
0.626 |
| Jon99Cii |
0.203 |
-3.1741526 |
0.594 |
0.155 |
0.699 |
| cib |
0.793 |
1.8690538 |
0.586 |
0.716 |
0.303 |
| bun |
0.793 |
1.4059197 |
0.586 |
0.809 |
0.419 |
| aqz |
0.787 |
0.9852902 |
0.574 |
0.869 |
0.488 |
| Pebp1 |
0.223 |
-2.7887394 |
0.554 |
0.095 |
0.638 |
| Tet |
0.774 |
1.9552201 |
0.548 |
0.607 |
0.167 |
Cluster 4
markers_roc4 = FindMarkers(integrated, ident.1=4, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc4, file.path("results", "markers_roc", "cluster4-markers_roc.csv"))
knitr::kable(head(markers_roc4, 25))
| Bace |
0.948 |
1.6662209 |
0.896 |
1.000 |
0.657 |
| betaTry |
0.915 |
1.3489190 |
0.830 |
1.000 |
0.693 |
| alphaTry |
0.914 |
1.2351138 |
0.828 |
1.000 |
0.820 |
| CG30025 |
0.908 |
1.3850072 |
0.816 |
1.000 |
0.652 |
| CG7542 |
0.902 |
1.9216820 |
0.804 |
0.917 |
0.277 |
| Diedel3 |
0.890 |
1.4456951 |
0.780 |
0.986 |
0.462 |
| Mal-A7 |
0.874 |
2.5066108 |
0.748 |
0.846 |
0.268 |
| CG5107 |
0.873 |
1.3861378 |
0.746 |
0.976 |
0.488 |
| epsilonTry |
0.865 |
1.3547922 |
0.730 |
0.967 |
0.434 |
| deltaTry |
0.861 |
1.3768449 |
0.722 |
0.974 |
0.504 |
| CG30031 |
0.861 |
1.3478686 |
0.722 |
0.972 |
0.507 |
| gammaTry |
0.861 |
1.3478686 |
0.722 |
0.972 |
0.507 |
| Jon65Aiv |
0.851 |
1.0712409 |
0.702 |
0.992 |
0.674 |
| Mal-A1 |
0.850 |
1.6094906 |
0.700 |
0.919 |
0.459 |
| Pebp1 |
0.843 |
1.2475968 |
0.686 |
0.974 |
0.567 |
| CG8834 |
0.833 |
1.2595427 |
0.666 |
0.873 |
0.261 |
| Jon65Aiii |
0.825 |
0.8930227 |
0.650 |
0.991 |
0.676 |
| CG4377 |
0.812 |
1.3900148 |
0.624 |
0.820 |
0.302 |
| Cyp4e1 |
0.808 |
1.5399858 |
0.616 |
0.788 |
0.314 |
| CG12374 |
0.777 |
0.8567660 |
0.554 |
0.946 |
0.665 |
| tobi |
0.774 |
1.5496700 |
0.548 |
0.744 |
0.289 |
| Mal-A8 |
0.766 |
1.4995523 |
0.532 |
0.663 |
0.198 |
| MtnA |
0.239 |
-2.7459049 |
0.522 |
0.410 |
0.731 |
| CG4363 |
0.756 |
1.3258361 |
0.512 |
0.683 |
0.231 |
| Mal-A6 |
0.745 |
1.1122476 |
0.490 |
0.677 |
0.256 |
Cluster 5
markers_roc5 = FindMarkers(integrated, ident.1=5, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc5, file.path("results", "markers_roc", "cluster5-markers_roc.csv"))
knitr::kable(head(markers_roc5, 25))
| LManVI |
0.977 |
3.2826160 |
0.954 |
0.979 |
0.243 |
| LManV |
0.942 |
2.9089739 |
0.884 |
0.915 |
0.111 |
| CG31343 |
0.941 |
2.0399043 |
0.882 |
0.972 |
0.294 |
| ninaD |
0.906 |
2.6390118 |
0.812 |
0.871 |
0.126 |
| LManII |
0.904 |
2.1743168 |
0.808 |
0.869 |
0.141 |
| CG15534 |
0.887 |
2.2950064 |
0.774 |
0.852 |
0.135 |
| iotaTry |
0.868 |
1.8477332 |
0.736 |
0.836 |
0.170 |
| Cyp6d5 |
0.865 |
1.6516791 |
0.730 |
0.891 |
0.332 |
| Mal-A4 |
0.859 |
1.9131132 |
0.718 |
0.813 |
0.161 |
| CG11911 |
0.855 |
1.2775764 |
0.710 |
0.940 |
0.346 |
| lambdaTry |
0.851 |
1.5033115 |
0.702 |
0.862 |
0.217 |
| Ance |
0.836 |
1.2035410 |
0.672 |
0.882 |
0.282 |
| LManI |
0.834 |
1.7075755 |
0.668 |
0.760 |
0.109 |
| CG7025 |
0.834 |
1.7006117 |
0.668 |
0.792 |
0.132 |
| CG14629 |
0.831 |
1.4508569 |
0.662 |
0.741 |
0.135 |
| asRNA:CR45281 |
0.825 |
1.1363389 |
0.650 |
0.746 |
0.158 |
| CG9673 |
0.823 |
1.0930344 |
0.646 |
0.848 |
0.220 |
| LManIII |
0.821 |
2.2069193 |
0.642 |
0.697 |
0.052 |
| CG15533 |
0.814 |
1.4710121 |
0.628 |
0.688 |
0.063 |
| fabp |
0.806 |
0.9226544 |
0.612 |
0.929 |
0.461 |
| CG33127 |
0.800 |
1.1443035 |
0.600 |
0.836 |
0.275 |
| Gba1a |
0.800 |
1.1219500 |
0.600 |
0.660 |
0.082 |
| CG11151 |
0.800 |
0.9555007 |
0.600 |
0.859 |
0.307 |
| CHMP2B |
0.800 |
0.9366572 |
0.600 |
0.704 |
0.218 |
| CG33306 |
0.798 |
1.1217776 |
0.596 |
0.718 |
0.123 |
Cluster 6
markers_roc6 = FindMarkers(integrated, ident.1=6, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc6, file.path("results", "markers_roc", "cluster6-markers_roc.csv"))
knitr::kable(head(markers_roc6, 25))
| MtnC |
0.882 |
1.7872526 |
0.764 |
0.973 |
0.675 |
| Arc1 |
0.867 |
2.4032135 |
0.734 |
0.911 |
0.374 |
| CG43774 |
0.855 |
3.1529264 |
0.710 |
0.807 |
0.207 |
| CG34330 |
0.838 |
1.4639846 |
0.676 |
0.945 |
0.655 |
| CG5399 |
0.837 |
1.6595736 |
0.674 |
0.857 |
0.351 |
| mbl |
0.834 |
2.1404858 |
0.668 |
0.850 |
0.388 |
| MtnD |
0.812 |
2.6718753 |
0.624 |
0.775 |
0.307 |
| MtnA |
0.811 |
1.4680532 |
0.622 |
0.959 |
0.693 |
| CG15423 |
0.807 |
1.9412040 |
0.614 |
0.737 |
0.166 |
| CG15422 |
0.776 |
1.7420240 |
0.552 |
0.730 |
0.245 |
| Stat92E |
0.769 |
1.7067355 |
0.538 |
0.631 |
0.212 |
| Vha16-1 |
0.754 |
0.9391610 |
0.508 |
0.952 |
0.783 |
| MtnB |
0.750 |
1.6650008 |
0.500 |
0.708 |
0.303 |
| Vha13 |
0.739 |
0.9406012 |
0.478 |
0.900 |
0.669 |
| Adat1 |
0.738 |
1.0285194 |
0.476 |
0.640 |
0.249 |
| thetaTry |
0.730 |
1.9437544 |
0.460 |
0.623 |
0.136 |
| CycG |
0.726 |
1.2622215 |
0.452 |
0.812 |
0.503 |
| MtnE |
0.723 |
1.6779141 |
0.446 |
0.642 |
0.249 |
| pre-rRNA:CR45845 |
0.279 |
-1.2473915 |
0.442 |
0.621 |
0.826 |
| RpLP2 |
0.720 |
0.6403127 |
0.440 |
0.986 |
0.949 |
| pre-rRNA:CR45847 |
0.281 |
-1.3870317 |
0.438 |
0.571 |
0.755 |
| CG8177 |
0.715 |
2.2074678 |
0.430 |
0.574 |
0.251 |
| Msp300 |
0.709 |
1.2365695 |
0.418 |
0.719 |
0.365 |
| Lk6 |
0.705 |
1.2882895 |
0.410 |
0.769 |
0.492 |
| RpL41 |
0.705 |
0.5436570 |
0.410 |
0.998 |
0.972 |
Cluster 7
markers_roc7 = FindMarkers(integrated, ident.1=7, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc7, file.path("results", "markers_roc", "cluster7-markers_roc.csv"))
knitr::kable(head(markers_roc7, 25))
| CG10472 |
0.984 |
2.616301 |
0.968 |
1.000 |
0.405 |
| Jon99Fii |
0.934 |
3.236508 |
0.868 |
0.940 |
0.255 |
| Jon25Biii |
0.926 |
3.256755 |
0.852 |
0.960 |
0.411 |
| CG17571 |
0.922 |
2.704814 |
0.844 |
0.936 |
0.257 |
| mag |
0.922 |
2.295322 |
0.844 |
0.929 |
0.172 |
| Acbp5 |
0.917 |
1.379220 |
0.834 |
0.998 |
0.670 |
| CG11911 |
0.904 |
2.168190 |
0.808 |
0.933 |
0.348 |
| Jon65Ai |
0.896 |
2.885554 |
0.792 |
0.858 |
0.142 |
| Jon99Fi |
0.880 |
3.266696 |
0.760 |
0.862 |
0.228 |
| CG7953 |
0.861 |
2.720265 |
0.722 |
0.800 |
0.203 |
| CG16749 |
0.858 |
1.553809 |
0.716 |
0.894 |
0.276 |
| CG17633 |
0.855 |
2.406228 |
0.710 |
0.811 |
0.222 |
| CG3868 |
0.849 |
1.400003 |
0.698 |
0.902 |
0.330 |
| CG8997 |
0.845 |
3.178884 |
0.690 |
0.801 |
0.263 |
| Jon65Aii |
0.836 |
3.293820 |
0.672 |
0.814 |
0.210 |
| lncRNA:CR40469 |
0.170 |
-2.088592 |
0.660 |
0.718 |
0.925 |
| CG31198 |
0.829 |
1.287501 |
0.658 |
0.885 |
0.290 |
| CG7916 |
0.821 |
3.257496 |
0.642 |
0.750 |
0.252 |
| Jon99Cii |
0.821 |
1.725367 |
0.642 |
0.954 |
0.642 |
| CG18493 |
0.820 |
1.598612 |
0.640 |
0.794 |
0.239 |
| CG31323 |
0.810 |
1.136244 |
0.620 |
0.836 |
0.322 |
| Jon99Ciii |
0.809 |
1.736809 |
0.618 |
0.965 |
0.725 |
| CG8093 |
0.802 |
1.531794 |
0.604 |
0.670 |
0.092 |
| asRNA:CR45281 |
0.802 |
1.095471 |
0.604 |
0.709 |
0.161 |
| CG31233 |
0.799 |
1.109342 |
0.598 |
0.829 |
0.263 |
Cluster 8
markers_roc8 = FindMarkers(integrated, ident.1=8, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc8, file.path("results", "markers_roc", "cluster8-markers_roc.csv"))
knitr::kable(head(markers_roc8, 25))
| Vha16-1 |
0.965 |
2.334783 |
0.930 |
1.000 |
0.783 |
| CG5767 |
0.963 |
4.182364 |
0.926 |
0.949 |
0.170 |
| Vha13 |
0.942 |
1.866896 |
0.884 |
0.996 |
0.667 |
| MtnB |
0.940 |
3.424423 |
0.880 |
0.947 |
0.297 |
| CG30479 |
0.914 |
3.367492 |
0.828 |
0.898 |
0.135 |
| Adat1 |
0.909 |
2.418973 |
0.818 |
0.889 |
0.242 |
| CG15423 |
0.907 |
2.526331 |
0.814 |
0.895 |
0.165 |
| Vha55 |
0.899 |
2.306491 |
0.798 |
0.958 |
0.416 |
| CG30480 |
0.893 |
2.297150 |
0.786 |
0.802 |
0.056 |
| Vha68-2 |
0.892 |
2.124161 |
0.784 |
0.931 |
0.341 |
| CG5399 |
0.887 |
2.160550 |
0.774 |
0.913 |
0.354 |
| Vha44 |
0.886 |
2.244559 |
0.772 |
0.964 |
0.474 |
| Vha14-1 |
0.883 |
1.918986 |
0.766 |
0.900 |
0.299 |
| VhaAC39-1 |
0.881 |
2.244777 |
0.762 |
0.913 |
0.328 |
| CG15422 |
0.868 |
1.932872 |
0.736 |
0.884 |
0.243 |
| Tsp42Ec |
0.865 |
2.502074 |
0.730 |
0.833 |
0.179 |
| VhaSFD |
0.864 |
2.068645 |
0.728 |
0.869 |
0.244 |
| Vha26 |
0.849 |
1.874010 |
0.698 |
0.878 |
0.308 |
| Vha36-1 |
0.845 |
1.801660 |
0.690 |
0.862 |
0.304 |
| Argk |
0.844 |
1.768606 |
0.688 |
0.909 |
0.389 |
| VhaAC45 |
0.838 |
1.735004 |
0.676 |
0.878 |
0.320 |
| Mpcp2 |
0.833 |
1.875708 |
0.666 |
0.951 |
0.558 |
| VhaM9.7-b |
0.832 |
1.618574 |
0.664 |
0.842 |
0.307 |
| CG7430 |
0.827 |
2.010335 |
0.654 |
0.793 |
0.292 |
| CAH1 |
0.824 |
2.278490 |
0.648 |
0.777 |
0.132 |
Cluster 9
markers_roc9 = FindMarkers(integrated, ident.1=9, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc9, file.path("results", "markers_roc", "cluster9-markers_roc.csv"))
knitr::kable(head(markers_roc9, 25))
| Amy-p |
0.927 |
3.7546156 |
0.854 |
0.926 |
0.413 |
| Amy-d |
0.823 |
3.4545206 |
0.646 |
0.718 |
0.233 |
| CG11400 |
0.820 |
3.3465943 |
0.640 |
0.688 |
0.146 |
| lncRNA:CR40469 |
0.811 |
0.6702035 |
0.622 |
0.995 |
0.911 |
| CG4928 |
0.804 |
3.3092892 |
0.608 |
0.678 |
0.197 |
| CG3819 |
0.803 |
3.3652770 |
0.606 |
0.657 |
0.126 |
| CG15043 |
0.793 |
3.0710478 |
0.586 |
0.642 |
0.159 |
| lncRNA:CR34335 |
0.781 |
0.7487954 |
0.562 |
0.980 |
0.813 |
| CG6839 |
0.766 |
3.6581995 |
0.532 |
0.622 |
0.156 |
| trol |
0.755 |
0.7098187 |
0.510 |
0.995 |
0.990 |
| fmt |
0.749 |
0.6997454 |
0.498 |
0.997 |
0.995 |
| CG14125 |
0.742 |
4.9471279 |
0.484 |
0.563 |
0.197 |
| CG13323 |
0.740 |
2.4128346 |
0.480 |
0.612 |
0.282 |
| Mal-A1 |
0.283 |
-1.8074388 |
0.434 |
0.124 |
0.508 |
| Vha13 |
0.712 |
0.6736012 |
0.424 |
0.883 |
0.673 |
| 28SrRNA-Psi:CR40741 |
0.292 |
-1.5479371 |
0.416 |
0.124 |
0.464 |
| Adhr |
0.708 |
1.6133663 |
0.416 |
0.541 |
0.202 |
| alphaTry |
0.295 |
-1.7875947 |
0.410 |
0.761 |
0.836 |
| CG30025 |
0.295 |
-1.8352758 |
0.410 |
0.393 |
0.689 |
| CG15818 |
0.704 |
3.2587424 |
0.408 |
0.492 |
0.108 |
| sesB |
0.703 |
0.6889130 |
0.406 |
0.893 |
0.772 |
| Oda |
0.699 |
0.7258382 |
0.398 |
0.919 |
0.830 |
| pAbp |
0.695 |
0.5943689 |
0.390 |
0.898 |
0.761 |
| Vha16-1 |
0.692 |
0.2603364 |
0.384 |
0.942 |
0.786 |
| Adh |
0.690 |
1.5147031 |
0.380 |
0.503 |
0.189 |
Cluster 10
markers_roc10 = FindMarkers(integrated, ident.1=10, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc10, file.path("results", "markers_roc", "cluster10-markers_roc.csv"))
knitr::kable(head(markers_roc10, 25))
| pre-rRNA:CR45846 |
0.883 |
1.8543423 |
0.766 |
0.967 |
0.592 |
| pre-rRNA:CR45847 |
0.877 |
1.6122745 |
0.754 |
0.994 |
0.737 |
| 18SrRNA:CR41548 |
0.875 |
1.8092135 |
0.750 |
0.917 |
0.516 |
| 18SrRNA:CR45838 |
0.875 |
1.8088839 |
0.750 |
0.917 |
0.516 |
| 18SrRNA:CR45841 |
0.869 |
1.8323186 |
0.738 |
0.902 |
0.506 |
| pre-rRNA:CR45845 |
0.833 |
1.5562434 |
0.666 |
0.979 |
0.810 |
| pre-rRNA:CR45856 |
0.828 |
1.9342579 |
0.656 |
0.842 |
0.497 |
| 18SrRNA-Psi:CR45861 |
0.807 |
1.9929792 |
0.614 |
0.774 |
0.425 |
| 18SrRNA-Psi:CR41602 |
0.768 |
1.9522780 |
0.536 |
0.714 |
0.415 |
| 28SrRNA:CR45844 |
0.715 |
1.1993126 |
0.430 |
0.744 |
0.607 |
| 28SrRNA-Psi:CR45851 |
0.702 |
1.3256885 |
0.404 |
0.688 |
0.531 |
| Jon99Cii |
0.702 |
0.5646288 |
0.404 |
0.893 |
0.650 |
| Jon99Ciii |
0.681 |
0.2742590 |
0.362 |
0.938 |
0.731 |
| CG12374 |
0.678 |
0.3401385 |
0.356 |
0.943 |
0.678 |
| Bace |
0.670 |
0.2581049 |
0.340 |
0.923 |
0.674 |
| CG30025 |
0.662 |
0.2913271 |
0.324 |
0.899 |
0.671 |
| 14-3-3epsilon |
0.347 |
-0.6633088 |
0.306 |
0.158 |
0.488 |
| 28SrRNA-Psi:CR40741 |
0.649 |
1.3577011 |
0.298 |
0.565 |
0.448 |
| 28SrRNA-Psi:CR41609 |
0.642 |
1.3964064 |
0.284 |
0.560 |
0.459 |
| clos |
0.364 |
-0.3143394 |
0.272 |
0.107 |
0.384 |
| CG33995 |
0.366 |
-0.3927518 |
0.268 |
0.071 |
0.341 |
| crc |
0.366 |
-0.3928422 |
0.268 |
0.131 |
0.412 |
| His3.3B |
0.366 |
-0.4291378 |
0.268 |
0.304 |
0.605 |
| CG10472 |
0.633 |
0.3546737 |
0.266 |
0.601 |
0.430 |
| CG42322 |
0.369 |
-0.2620372 |
0.262 |
0.182 |
0.471 |
Cluster 11
markers_roc11 = FindMarkers(integrated, ident.1=11, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc11, file.path("results", "markers_roc", "cluster11-markers_roc.csv"))
knitr::kable(head(markers_roc11, 25))
| Gs2 |
0.960 |
2.5571275 |
0.920 |
0.978 |
0.369 |
| Acbp3 |
0.957 |
2.7989831 |
0.914 |
0.978 |
0.298 |
| CG31343 |
0.927 |
1.8074765 |
0.854 |
0.984 |
0.310 |
| zetaTry |
0.902 |
2.2494411 |
0.804 |
0.873 |
0.180 |
| CG13492 |
0.891 |
1.8982504 |
0.782 |
0.876 |
0.217 |
| CG9673 |
0.880 |
1.7049865 |
0.760 |
0.895 |
0.234 |
| fabp |
0.864 |
1.2838541 |
0.728 |
0.962 |
0.471 |
| CG15254 |
0.842 |
2.5365965 |
0.684 |
0.796 |
0.138 |
| MtnA |
0.842 |
1.1704878 |
0.684 |
0.987 |
0.699 |
| CG10116 |
0.837 |
1.7645785 |
0.674 |
0.812 |
0.219 |
| Gdh |
0.837 |
1.7188263 |
0.674 |
0.768 |
0.192 |
| CG8774 |
0.834 |
2.0324823 |
0.668 |
0.768 |
0.148 |
| Agpat4 |
0.819 |
1.5228590 |
0.638 |
0.761 |
0.194 |
| CG32473 |
0.817 |
2.6193899 |
0.634 |
0.739 |
0.143 |
| CG5958 |
0.801 |
1.7785181 |
0.602 |
0.726 |
0.177 |
| Pepck2 |
0.795 |
1.7380777 |
0.590 |
0.653 |
0.096 |
| NAAT1 |
0.790 |
1.9975076 |
0.580 |
0.669 |
0.058 |
| dmGlut |
0.790 |
1.4212022 |
0.580 |
0.653 |
0.092 |
| CG10911 |
0.789 |
0.9464320 |
0.578 |
0.898 |
0.402 |
| CG32633 |
0.788 |
1.2566876 |
0.576 |
0.758 |
0.236 |
| Jon99Ciii |
0.215 |
-3.9366132 |
0.570 |
0.455 |
0.746 |
| Jon65Aiv |
0.218 |
-3.0364897 |
0.564 |
0.366 |
0.708 |
| CG4653 |
0.781 |
1.4001194 |
0.562 |
0.736 |
0.220 |
| Acbp5 |
0.779 |
0.7779398 |
0.558 |
0.978 |
0.679 |
| Jon65Aiii |
0.221 |
-3.0295947 |
0.558 |
0.360 |
0.710 |
Cluster 12
markers_roc12 = FindMarkers(integrated, ident.1=12, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc12, file.path("results", "markers_roc", "cluster12-markers_roc.csv"))
knitr::kable(head(markers_roc12, 25))
| IA-2 |
0.964 |
2.647187 |
0.928 |
0.993 |
0.403 |
| nrv3 |
0.916 |
2.247964 |
0.832 |
0.909 |
0.242 |
| 7B2 |
0.913 |
2.096422 |
0.826 |
0.915 |
0.231 |
| Phm |
0.882 |
1.793405 |
0.764 |
0.866 |
0.234 |
| Pal2 |
0.852 |
2.079188 |
0.704 |
0.782 |
0.147 |
| pros |
0.844 |
2.015971 |
0.688 |
0.769 |
0.181 |
| w |
0.841 |
2.291362 |
0.682 |
0.759 |
0.206 |
| CG30183 |
0.838 |
1.964119 |
0.676 |
0.736 |
0.130 |
| alphaTry |
0.172 |
-3.018572 |
0.656 |
0.440 |
0.845 |
| Jon99Ciii |
0.179 |
-3.040954 |
0.642 |
0.212 |
0.753 |
| Hsp83 |
0.813 |
1.332282 |
0.626 |
0.850 |
0.374 |
| Jon65Aiv |
0.195 |
-2.829406 |
0.610 |
0.186 |
0.714 |
| betaTry |
0.195 |
-3.142426 |
0.610 |
0.215 |
0.731 |
| heph |
0.804 |
1.849458 |
0.608 |
0.664 |
0.104 |
| Galphao |
0.803 |
1.910813 |
0.606 |
0.691 |
0.175 |
| Jon65Aiii |
0.200 |
-2.671267 |
0.600 |
0.199 |
0.715 |
| RpL8 |
0.201 |
-1.069680 |
0.598 |
0.560 |
0.903 |
| Gbeta13F |
0.798 |
1.369896 |
0.596 |
0.779 |
0.335 |
| Bace |
0.202 |
-2.942945 |
0.596 |
0.160 |
0.698 |
| RpL35 |
0.203 |
-1.058824 |
0.594 |
0.564 |
0.904 |
| RpL37A |
0.207 |
-1.029812 |
0.586 |
0.664 |
0.925 |
| CG12374 |
0.207 |
-3.043524 |
0.586 |
0.189 |
0.701 |
| RpL17 |
0.211 |
-1.002210 |
0.578 |
0.560 |
0.895 |
| Jon99Cii |
0.216 |
-2.491872 |
0.568 |
0.147 |
0.673 |
| CG30025 |
0.216 |
-3.165431 |
0.568 |
0.238 |
0.691 |
Cluster 13
markers_roc13 = FindMarkers(integrated, ident.1=13, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc13, file.path("results", "markers_roc", "cluster13-markers_roc.csv"))
knitr::kable(head(markers_roc13, 25))
| CG33926 |
0.924 |
2.1060157 |
0.848 |
0.950 |
0.228 |
| CG10912 |
0.911 |
2.2182013 |
0.822 |
0.907 |
0.203 |
| CG10911 |
0.891 |
1.5862509 |
0.782 |
0.963 |
0.401 |
| CG32368 |
0.883 |
2.1692952 |
0.766 |
0.854 |
0.168 |
| Mur29B |
0.855 |
2.5662759 |
0.710 |
0.804 |
0.184 |
| CG42825 |
0.844 |
2.2269327 |
0.688 |
0.761 |
0.116 |
| CG14499 |
0.843 |
3.0172332 |
0.686 |
0.764 |
0.075 |
| lectin-37Da |
0.839 |
2.7082242 |
0.678 |
0.757 |
0.081 |
| CG31086 |
0.823 |
1.5386260 |
0.646 |
0.804 |
0.311 |
| CG31323 |
0.821 |
1.7430702 |
0.642 |
0.837 |
0.335 |
| CG18493 |
0.794 |
1.4654841 |
0.588 |
0.754 |
0.253 |
| RpL3 |
0.213 |
-0.9937845 |
0.574 |
0.718 |
0.896 |
| eEF1alpha1 |
0.218 |
-0.8581779 |
0.564 |
0.887 |
0.970 |
| Npc2e |
0.778 |
2.1977119 |
0.556 |
0.691 |
0.154 |
| CG15255 |
0.778 |
1.3555371 |
0.556 |
0.771 |
0.267 |
| asRNA:CR44192 |
0.778 |
1.3426450 |
0.556 |
0.635 |
0.150 |
| Fer2LCH |
0.772 |
0.9810805 |
0.544 |
0.897 |
0.479 |
| RpS29 |
0.238 |
-0.8599220 |
0.524 |
0.837 |
0.941 |
| lectin-37Db |
0.757 |
1.3335771 |
0.514 |
0.688 |
0.226 |
| CG9568 |
0.754 |
1.6351462 |
0.508 |
0.688 |
0.224 |
| CG44008 |
0.754 |
0.6956464 |
0.508 |
0.924 |
0.571 |
| RpS2 |
0.247 |
-0.9726861 |
0.506 |
0.731 |
0.873 |
| sta |
0.249 |
-0.9397450 |
0.502 |
0.721 |
0.889 |
| RpL14 |
0.250 |
-0.9298185 |
0.500 |
0.638 |
0.842 |
| RpL38 |
0.254 |
-0.9220046 |
0.492 |
0.585 |
0.843 |
Cluster 14
markers_roc14 = FindMarkers(integrated, ident.1=14, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc14, file.path("results", "markers_roc", "cluster14-markers_roc.csv"))
knitr::kable(head(markers_roc14, 25))
| NPF |
0.989 |
5.147902 |
0.978 |
0.983 |
0.290 |
| IA-2 |
0.986 |
3.213505 |
0.972 |
1.000 |
0.403 |
| 7B2 |
0.933 |
3.044842 |
0.866 |
0.924 |
0.231 |
| Phm |
0.913 |
2.665425 |
0.826 |
0.890 |
0.234 |
| Tk |
0.898 |
2.448163 |
0.796 |
0.857 |
0.210 |
| svr |
0.896 |
2.567417 |
0.792 |
0.867 |
0.243 |
| chrb |
0.884 |
3.500329 |
0.768 |
0.841 |
0.281 |
| nrv3 |
0.867 |
2.251026 |
0.734 |
0.827 |
0.245 |
| CG30183 |
0.852 |
2.181997 |
0.704 |
0.764 |
0.129 |
| CG46385 |
0.850 |
2.210807 |
0.700 |
0.831 |
0.325 |
| esg |
0.840 |
2.222163 |
0.680 |
0.761 |
0.184 |
| unc-13-4A |
0.834 |
1.715761 |
0.668 |
0.757 |
0.169 |
| Hsp23 |
0.827 |
2.841930 |
0.654 |
0.781 |
0.318 |
| Ldh |
0.827 |
2.230674 |
0.654 |
0.728 |
0.177 |
| alphaTry |
0.177 |
-2.887524 |
0.646 |
0.482 |
0.844 |
| cpo |
0.822 |
2.446349 |
0.644 |
0.761 |
0.275 |
| Pal2 |
0.822 |
2.156723 |
0.644 |
0.748 |
0.149 |
| Hsp22 |
0.822 |
1.852289 |
0.644 |
0.777 |
0.341 |
| mbl |
0.821 |
1.258315 |
0.642 |
0.867 |
0.399 |
| Hsp83 |
0.820 |
1.744655 |
0.640 |
0.847 |
0.374 |
| Unr |
0.815 |
1.970741 |
0.630 |
0.804 |
0.377 |
| Hsp26 |
0.809 |
1.839010 |
0.618 |
0.847 |
0.432 |
| Ih |
0.807 |
2.434480 |
0.614 |
0.661 |
0.097 |
| shep |
0.807 |
1.896214 |
0.614 |
0.728 |
0.241 |
| CG30025 |
0.195 |
-2.890765 |
0.610 |
0.199 |
0.692 |
Cluster 15
markers_roc15 = FindMarkers(integrated, ident.1=15, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc15, file.path("results", "markers_roc", "cluster15-markers_roc.csv"))
knitr::kable(head(markers_roc15, 25))
| Orcokinin |
1.000 |
5.327171 |
1.000 |
1.000 |
0.292 |
| AstC |
0.987 |
3.333166 |
0.974 |
0.996 |
0.293 |
| svr |
0.965 |
3.009325 |
0.930 |
0.979 |
0.244 |
| unc-13-4A |
0.957 |
2.946501 |
0.914 |
0.963 |
0.167 |
| nrv3 |
0.939 |
2.642404 |
0.878 |
0.954 |
0.246 |
| IA-2 |
0.936 |
2.182955 |
0.872 |
0.992 |
0.407 |
| 7B2 |
0.927 |
2.126905 |
0.854 |
0.959 |
0.235 |
| Phm |
0.923 |
2.484770 |
0.846 |
0.934 |
0.237 |
| tap |
0.917 |
1.980146 |
0.834 |
0.851 |
0.030 |
| heph |
0.898 |
2.319493 |
0.796 |
0.846 |
0.103 |
| CG30183 |
0.898 |
1.741654 |
0.796 |
0.880 |
0.130 |
| pros |
0.896 |
2.421778 |
0.792 |
0.871 |
0.183 |
| CG14989 |
0.884 |
3.799407 |
0.768 |
0.871 |
0.190 |
| Ca-alpha1T |
0.880 |
2.284545 |
0.760 |
0.788 |
0.055 |
| slo |
0.880 |
1.877297 |
0.760 |
0.780 |
0.045 |
| Rgk3 |
0.879 |
2.170133 |
0.758 |
0.768 |
0.022 |
| jus |
0.873 |
1.609255 |
0.746 |
0.768 |
0.051 |
| Pal2 |
0.871 |
2.284061 |
0.742 |
0.846 |
0.150 |
| epsilonTry |
0.129 |
-2.541908 |
0.742 |
0.046 |
0.484 |
| lncRNA:CR31451 |
0.870 |
1.543013 |
0.740 |
0.780 |
0.062 |
| fkh |
0.867 |
1.852307 |
0.734 |
0.871 |
0.279 |
| CG44247 |
0.867 |
1.801113 |
0.734 |
0.780 |
0.060 |
| shep |
0.864 |
1.872548 |
0.728 |
0.876 |
0.241 |
| CG46385 |
0.859 |
1.946008 |
0.718 |
0.905 |
0.327 |
| fru |
0.859 |
1.606407 |
0.718 |
0.780 |
0.144 |
Cluster 16
markers_roc16 = FindMarkers(integrated, ident.1=16, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc16, file.path("results", "markers_roc", "cluster16-markers_roc.csv"))
knitr::kable(head(markers_roc16, 25))
| EbpIII |
0.948 |
4.834704 |
0.896 |
0.920 |
0.223 |
| Acbp2 |
0.914 |
2.549511 |
0.828 |
0.903 |
0.364 |
| lncRNA:CR34335 |
0.905 |
1.580156 |
0.810 |
1.000 |
0.815 |
| Adhr |
0.883 |
3.095023 |
0.766 |
0.819 |
0.200 |
| Adh |
0.861 |
3.083127 |
0.722 |
0.776 |
0.187 |
| lncRNA:CR40469 |
0.858 |
1.202531 |
0.716 |
1.000 |
0.912 |
| to |
0.853 |
3.587573 |
0.706 |
0.726 |
0.064 |
| spidey |
0.841 |
3.329530 |
0.682 |
0.734 |
0.216 |
| CG30197 |
0.825 |
3.532219 |
0.650 |
0.684 |
0.094 |
| CG31523 |
0.824 |
2.969051 |
0.648 |
0.658 |
0.148 |
| CG31522 |
0.807 |
3.284502 |
0.614 |
0.688 |
0.226 |
| CG1124 |
0.786 |
3.623984 |
0.572 |
0.620 |
0.110 |
| Msr-110 |
0.779 |
2.666640 |
0.558 |
0.612 |
0.116 |
| CG6770 |
0.768 |
1.183859 |
0.536 |
0.819 |
0.557 |
| ple |
0.763 |
2.881654 |
0.526 |
0.553 |
0.030 |
| wat |
0.756 |
3.231272 |
0.512 |
0.532 |
0.046 |
| CG10237 |
0.750 |
2.613266 |
0.500 |
0.536 |
0.048 |
| Moe |
0.748 |
1.735258 |
0.496 |
0.591 |
0.240 |
| emp |
0.744 |
2.438345 |
0.488 |
0.523 |
0.089 |
| vir-1 |
0.738 |
2.554232 |
0.476 |
0.494 |
0.033 |
| Hacd1 |
0.737 |
2.129050 |
0.474 |
0.485 |
0.111 |
| CG8306 |
0.734 |
2.509803 |
0.468 |
0.494 |
0.060 |
| ATPCL |
0.730 |
2.223677 |
0.460 |
0.527 |
0.170 |
| Inos |
0.729 |
2.157946 |
0.458 |
0.502 |
0.099 |
| Taldo |
0.727 |
1.879034 |
0.454 |
0.523 |
0.197 |
Cluster 17
markers_roc17 = FindMarkers(integrated, ident.1=17, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc17, file.path("results", "markers_roc", "cluster17-markers_roc.csv"))
knitr::kable(head(markers_roc17, 25))
| CG14645 |
0.999 |
5.166353 |
0.998 |
1.000 |
0.282 |
| Skp2 |
0.998 |
5.300042 |
0.996 |
1.000 |
0.389 |
| CG3906 |
0.992 |
5.087155 |
0.984 |
0.991 |
0.217 |
| CG34324 |
0.990 |
5.281636 |
0.980 |
0.987 |
0.277 |
| CG34220 |
0.988 |
5.093672 |
0.976 |
0.987 |
0.420 |
| CG4783 |
0.971 |
3.204050 |
0.942 |
0.956 |
0.160 |
| Muc68D |
0.958 |
5.030528 |
0.916 |
0.933 |
0.207 |
| CG11672 |
0.932 |
3.842805 |
0.864 |
0.871 |
0.037 |
| Idgf4 |
0.874 |
2.455162 |
0.748 |
0.782 |
0.072 |
| Pgant4 |
0.828 |
2.450677 |
0.656 |
0.662 |
0.014 |
| alphaTry |
0.186 |
-2.869711 |
0.628 |
0.440 |
0.842 |
| CG30025 |
0.186 |
-3.163239 |
0.628 |
0.116 |
0.690 |
| Jon99Ciii |
0.201 |
-2.909744 |
0.598 |
0.227 |
0.749 |
| CG34330 |
0.214 |
-2.012285 |
0.572 |
0.133 |
0.682 |
| Jon65Aiv |
0.216 |
-2.543440 |
0.568 |
0.204 |
0.709 |
| Jon65Aiii |
0.217 |
-2.454261 |
0.566 |
0.209 |
0.710 |
| betaTry |
0.217 |
-2.727593 |
0.566 |
0.236 |
0.726 |
| Jon99Cii |
0.229 |
-2.470175 |
0.542 |
0.151 |
0.669 |
| Bace |
0.232 |
-2.477221 |
0.536 |
0.209 |
0.693 |
| Vha16-1 |
0.239 |
-1.611035 |
0.522 |
0.364 |
0.801 |
| MtnC |
0.239 |
-2.133976 |
0.522 |
0.280 |
0.700 |
| sesB |
0.240 |
-1.164468 |
0.520 |
0.320 |
0.787 |
| Act5C |
0.243 |
-1.032861 |
0.514 |
0.604 |
0.900 |
| CG12374 |
0.245 |
-2.491602 |
0.510 |
0.249 |
0.696 |
| CG30026 |
0.754 |
1.759105 |
0.508 |
0.511 |
0.003 |
Cluster 18
markers_roc18 = FindMarkers(integrated, ident.1=18, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc18, file.path("results", "markers_roc", "cluster18-markers_roc.csv"))
knitr::kable(head(markers_roc18, 25))
| Npc2f |
0.986 |
3.6101816 |
0.972 |
0.988 |
0.150 |
| Jon65Aiv |
0.986 |
2.1546865 |
0.972 |
1.000 |
0.694 |
| thetaTry |
0.980 |
3.0983381 |
0.960 |
0.988 |
0.149 |
| yip7 |
0.979 |
2.3389780 |
0.958 |
1.000 |
0.539 |
| Jon65Aiii |
0.979 |
2.0314714 |
0.958 |
1.000 |
0.695 |
| Peritrophin-15a |
0.938 |
2.6028515 |
0.876 |
0.950 |
0.184 |
| Bace |
0.926 |
1.8774625 |
0.852 |
0.994 |
0.678 |
| CG30031 |
0.915 |
1.3828164 |
0.830 |
1.000 |
0.535 |
| gammaTry |
0.915 |
1.3828164 |
0.830 |
1.000 |
0.535 |
| deltaTry |
0.890 |
1.3582514 |
0.780 |
1.000 |
0.532 |
| CG4734 |
0.885 |
1.8411515 |
0.770 |
0.826 |
0.132 |
| CG18404 |
0.879 |
4.0870292 |
0.758 |
0.845 |
0.161 |
| alphaTry |
0.872 |
1.0091274 |
0.744 |
1.000 |
0.831 |
| Pebp1 |
0.867 |
1.5259964 |
0.734 |
0.944 |
0.592 |
| CG9682 |
0.851 |
2.5655588 |
0.702 |
0.727 |
0.027 |
| CG30025 |
0.842 |
1.0353096 |
0.684 |
1.000 |
0.673 |
| LysB |
0.835 |
1.3202083 |
0.670 |
0.826 |
0.197 |
| CG4830 |
0.827 |
2.6994819 |
0.654 |
0.789 |
0.017 |
| lncRNA:CR40469 |
0.181 |
-2.0822384 |
0.638 |
0.826 |
0.915 |
| CG4563 |
0.816 |
2.4116003 |
0.632 |
0.770 |
0.061 |
| CG5107 |
0.799 |
0.8569231 |
0.598 |
0.963 |
0.518 |
| betaTry |
0.788 |
0.8541648 |
0.576 |
1.000 |
0.711 |
| CG34040 |
0.783 |
0.5422801 |
0.566 |
0.640 |
0.082 |
| CG4377 |
0.779 |
0.9930001 |
0.558 |
0.857 |
0.333 |
| epsilonTry |
0.778 |
0.7924996 |
0.556 |
0.957 |
0.467 |
Cluster 19
markers_roc19 = FindMarkers(integrated, ident.1=19, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc19, file.path("results", "markers_roc", "cluster19-markers_roc.csv"))
knitr::kable(head(markers_roc19, 25))
| CG6409 |
0.980 |
4.8036985 |
0.960 |
0.966 |
0.145 |
| CG42656 |
0.951 |
4.4368015 |
0.902 |
0.910 |
0.082 |
| IM33 |
0.908 |
3.9650892 |
0.816 |
0.831 |
0.081 |
| Vha13 |
0.906 |
1.5702548 |
0.812 |
0.978 |
0.679 |
| lncRNA:CR40469 |
0.902 |
1.3818689 |
0.804 |
1.000 |
0.913 |
| Vha16-1 |
0.892 |
1.1695161 |
0.784 |
0.978 |
0.791 |
| sesB |
0.880 |
1.2228950 |
0.760 |
0.978 |
0.775 |
| Vha55 |
0.845 |
1.5750791 |
0.690 |
0.876 |
0.436 |
| lncRNA:CR34335 |
0.842 |
1.0495865 |
0.684 |
0.989 |
0.817 |
| ATPsynC |
0.817 |
1.1686827 |
0.634 |
0.910 |
0.659 |
| Vha44 |
0.815 |
1.3559872 |
0.630 |
0.843 |
0.492 |
| Gdh |
0.811 |
2.5818285 |
0.622 |
0.685 |
0.205 |
| Vha26 |
0.808 |
1.6921151 |
0.616 |
0.764 |
0.328 |
| Vha68-2 |
0.805 |
1.5239685 |
0.610 |
0.775 |
0.362 |
| porin |
0.799 |
1.5868938 |
0.598 |
0.809 |
0.503 |
| tws |
0.797 |
2.6627324 |
0.594 |
0.640 |
0.170 |
| blw |
0.790 |
1.4953967 |
0.580 |
0.820 |
0.556 |
| Ggamma30A |
0.778 |
2.4257360 |
0.556 |
0.584 |
0.061 |
| CG1143 |
0.776 |
2.8464808 |
0.552 |
0.584 |
0.052 |
| Irk2 |
0.775 |
2.8819032 |
0.550 |
0.584 |
0.057 |
| Msr-110 |
0.764 |
2.2743860 |
0.528 |
0.596 |
0.123 |
| pAbp |
0.760 |
0.7985467 |
0.520 |
0.910 |
0.765 |
| Argk |
0.753 |
1.2657596 |
0.506 |
0.730 |
0.408 |
| Mpcp2 |
0.753 |
1.1475929 |
0.506 |
0.798 |
0.572 |
| Dic1 |
0.752 |
1.8342125 |
0.504 |
0.562 |
0.178 |
Cluster 20
markers_roc20 = FindMarkers(integrated, ident.1=20, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc20, file.path("results", "markers_roc", "cluster20-markers_roc.csv"))
knitr::kable(head(markers_roc20, 25))
| CG13285 |
0.980 |
3.8504561 |
0.960 |
0.963 |
0.020 |
| Msr-110 |
0.970 |
3.1788920 |
0.940 |
0.963 |
0.123 |
| CG44013 |
0.887 |
2.9005776 |
0.774 |
0.778 |
0.020 |
| lncRNA:CR34335 |
0.886 |
1.2155838 |
0.772 |
1.000 |
0.818 |
| Idgf4 |
0.885 |
2.3408393 |
0.770 |
0.815 |
0.083 |
| Idgf6 |
0.881 |
2.6238014 |
0.762 |
0.778 |
0.043 |
| lncRNA:CR40469 |
0.876 |
0.9434807 |
0.752 |
1.000 |
0.913 |
| CG5065 |
0.852 |
2.2587751 |
0.704 |
0.741 |
0.095 |
| CG43394 |
0.837 |
2.6414936 |
0.674 |
0.685 |
0.028 |
| CG10096 |
0.829 |
2.8104611 |
0.658 |
0.667 |
0.012 |
| mt:lrRNA |
0.819 |
0.3111689 |
0.638 |
1.000 |
1.000 |
| Vha16-1 |
0.186 |
-2.1373285 |
0.628 |
0.241 |
0.795 |
| MtnC |
0.201 |
-2.6273170 |
0.598 |
0.111 |
0.694 |
| Act5C |
0.202 |
-1.2459382 |
0.596 |
0.574 |
0.896 |
| Orcokinin |
0.794 |
0.5212523 |
0.588 |
0.741 |
0.306 |
| alphaTry |
0.208 |
-2.5242600 |
0.584 |
0.444 |
0.836 |
| Jon65Aiii |
0.212 |
-2.6360940 |
0.576 |
0.148 |
0.702 |
| Jon99Cii |
0.227 |
-2.9229030 |
0.546 |
0.093 |
0.661 |
| GstD11 |
0.768 |
2.0589475 |
0.536 |
0.537 |
0.001 |
| Jon65Aiv |
0.232 |
-2.4779429 |
0.536 |
0.204 |
0.701 |
| Acbp5 |
0.234 |
-1.7272450 |
0.532 |
0.148 |
0.690 |
| Cys |
0.765 |
1.8452262 |
0.530 |
0.611 |
0.177 |
| CG30025 |
0.240 |
-2.5843813 |
0.520 |
0.204 |
0.681 |
| Vha13 |
0.242 |
-1.7401562 |
0.516 |
0.167 |
0.684 |
| Atpalpha |
0.754 |
1.4416261 |
0.508 |
0.667 |
0.310 |
Cluster 21
markers_roc21 = FindMarkers(integrated, ident.1=21, test.use="roc") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(-power)
readr::write_csv(markers_roc21, file.path("results", "markers_roc", "cluster21-markers_roc.csv"))
knitr::kable(head(markers_roc21, 25))
| MtnA |
0.933 |
1.8253924 |
0.866 |
1.000 |
0.706 |
| Alp4 |
0.810 |
4.0091710 |
0.620 |
0.639 |
0.055 |
| Vha13 |
0.801 |
1.0715504 |
0.602 |
0.889 |
0.681 |
| CG6726 |
0.784 |
3.1950727 |
0.568 |
0.611 |
0.109 |
| Mur18B |
0.773 |
3.7634567 |
0.546 |
0.583 |
0.080 |
| Alp2 |
0.771 |
3.2290870 |
0.542 |
0.556 |
0.029 |
| sesB |
0.756 |
0.7051319 |
0.512 |
0.944 |
0.776 |
| CG3168 |
0.746 |
3.7752656 |
0.492 |
0.528 |
0.073 |
| Vha26 |
0.745 |
1.0301148 |
0.490 |
0.694 |
0.330 |
| alphaTry |
0.264 |
-1.4814880 |
0.472 |
0.556 |
0.835 |
| HDAC6 |
0.729 |
2.5162694 |
0.458 |
0.500 |
0.161 |
| Mal-A1 |
0.272 |
-2.6635592 |
0.456 |
0.028 |
0.495 |
| CG18324 |
0.723 |
1.7456577 |
0.446 |
0.444 |
0.126 |
| UGP |
0.721 |
2.2285553 |
0.442 |
0.500 |
0.160 |
| Vha16-1 |
0.721 |
0.6729296 |
0.442 |
0.889 |
0.792 |
| CG30025 |
0.286 |
-1.8776482 |
0.428 |
0.333 |
0.679 |
| smp-30 |
0.707 |
2.1958556 |
0.414 |
0.472 |
0.166 |
| Vha44 |
0.703 |
0.8803877 |
0.406 |
0.694 |
0.494 |
| CG31288 |
0.700 |
2.0329887 |
0.400 |
0.444 |
0.172 |
| CG30031 |
0.301 |
-1.3683171 |
0.398 |
0.167 |
0.543 |
| gammaTry |
0.301 |
-1.3683171 |
0.398 |
0.167 |
0.543 |
| CG13868 |
0.698 |
1.8833634 |
0.396 |
0.417 |
0.137 |
| deltaTry |
0.303 |
-1.3374017 |
0.394 |
0.167 |
0.540 |
| CG5107 |
0.303 |
-2.0925566 |
0.394 |
0.139 |
0.526 |
| Vha55 |
0.695 |
0.8688583 |
0.390 |
0.667 |
0.439 |
Specific markers-roc plot for cluster 21.
DimPlot(integrated, label=TRUE)

FeaturePlot(integrated, features=c("MtnA", "Vha13", "Smvt", "Oatp58Dc"))

cells21 = rownames(subset(integrated@meta.data, seurat_clusters == 21))
FeaturePlot(integrated[,cells21], features=c("MtnA", "Vha13", "Smvt", "Oatp58Dc")) +
ggtitle("cluster 21 only")

cluster markers (MAST)
Here we will find markers for each of the clusters where we haven’t been able to assign a cell type so we can hopefully figure out what type of cells they are.
Cluster 0
dir.create(file.path("results", "markers_MAST"))
markers_MAST0 = FindMarkers(integrated, ident.1=0, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST0, file.path("results", "markers_MAST", "cluster0-markers_MAST.csv"))
knitr::kable(head(markers_MAST0, 25))
| CG12374 |
0 |
0.7426282 |
1.000 |
0.604 |
0 |
| alphaTry |
0 |
0.5690638 |
1.000 |
0.790 |
0 |
| betaTry |
0 |
0.5517806 |
1.000 |
0.641 |
0 |
| Bace |
0 |
0.4316666 |
0.996 |
0.599 |
0 |
| CG30025 |
0 |
0.4160439 |
0.973 |
0.601 |
0 |
| Jon99Ciii |
0 |
0.3977139 |
0.999 |
0.669 |
0 |
| Jon99Cii |
0 |
0.3550315 |
0.975 |
0.574 |
0 |
| Jon65Aiii |
0 |
0.2787393 |
0.975 |
0.627 |
0 |
| pre-rRNA:CR45846 |
0 |
-0.9791249 |
0.243 |
0.699 |
0 |
| 18SrRNA:CR45838 |
0 |
-1.0210926 |
0.171 |
0.623 |
0 |
| 18SrRNA:CR41548 |
0 |
-1.0260107 |
0.170 |
0.623 |
0 |
| 18SrRNA:CR45841 |
0 |
-1.1015931 |
0.158 |
0.613 |
0 |
| pre-rRNA:CR45856 |
0 |
-1.5008458 |
0.133 |
0.607 |
0 |
| 18SrRNA-Psi:CR41602 |
0 |
-1.5761435 |
0.089 |
0.514 |
0 |
| 18SrRNA-Psi:CR45861 |
0 |
-1.5898494 |
0.091 |
0.528 |
0 |
| CG12057 |
0 |
0.6674180 |
0.624 |
0.298 |
0 |
| His3.3B |
0 |
-0.4862776 |
0.268 |
0.682 |
0 |
| Pebp1 |
0 |
0.3555240 |
0.876 |
0.524 |
0 |
| ps |
0 |
-0.3731687 |
0.233 |
0.625 |
0 |
| lncRNA:CR42491 |
0 |
-0.3863641 |
0.189 |
0.582 |
0 |
| 14-3-3epsilon |
0 |
-0.6482983 |
0.169 |
0.559 |
0 |
| cib |
0 |
-0.5655328 |
0.066 |
0.405 |
0 |
| CycG |
0 |
-0.8424565 |
0.207 |
0.601 |
0 |
| porin |
0 |
-0.3450815 |
0.217 |
0.582 |
0 |
| 28SrRNA-Psi:CR45862 |
0 |
-0.2617602 |
0.030 |
0.339 |
0 |
Cluster 1
dir.create(file.path("results", "markers_MAST"))
markers_MAST1 = FindMarkers(integrated, ident.1=1, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST1, file.path("results", "markers_MAST", "cluster1-markers_MAST.csv"))
knitr::kable(head(markers_MAST1, 25))
| Jon25Bi |
0 |
2.4973360 |
0.712 |
0.171 |
0 |
| CG6295 |
0 |
2.4180003 |
0.970 |
0.349 |
0 |
| CG17192 |
0 |
2.2434487 |
0.692 |
0.104 |
0 |
| CG34026 |
0 |
1.8928975 |
0.535 |
0.076 |
0 |
| CG12374 |
0 |
1.7278512 |
0.991 |
0.659 |
0 |
| CG30025 |
0 |
1.5042412 |
0.992 |
0.650 |
0 |
| betaTry |
0 |
1.4927853 |
0.992 |
0.691 |
0 |
| CG30031 |
0 |
1.4176099 |
0.954 |
0.505 |
0 |
| gammaTry |
0 |
1.4176099 |
0.954 |
0.505 |
0 |
| deltaTry |
0 |
1.3654433 |
0.947 |
0.502 |
0 |
| CG5107 |
0 |
1.4549836 |
0.935 |
0.488 |
0 |
| Jon99Cii |
0 |
1.2946068 |
0.978 |
0.629 |
0 |
| epsilonTry |
0 |
1.4368083 |
0.934 |
0.433 |
0 |
| Jon99Ciii |
0 |
1.3578745 |
0.989 |
0.715 |
0 |
| Jon25Bii |
0 |
2.2726902 |
0.610 |
0.146 |
0 |
| alphaTry |
0 |
1.2832729 |
0.992 |
0.819 |
0 |
| Jon99Ci |
0 |
1.3032560 |
0.822 |
0.325 |
0 |
| Mal-A6 |
0 |
1.6997284 |
0.706 |
0.250 |
0 |
| Jon65Aiii |
0 |
1.2721982 |
0.968 |
0.675 |
0 |
| Bace |
0 |
0.9766733 |
0.979 |
0.656 |
0 |
| Diedel3 |
0 |
1.2266437 |
0.879 |
0.467 |
0 |
| MtnA |
0 |
-2.4875220 |
0.519 |
0.724 |
0 |
| Amy-p |
0 |
0.8226741 |
0.848 |
0.395 |
0 |
| tobi |
0 |
1.6524216 |
0.749 |
0.284 |
0 |
| Acbp5 |
0 |
-2.2991966 |
0.396 |
0.714 |
0 |
Cluster 2
dir.create(file.path("results", "markers_MAST"))
markers_MAST2 = FindMarkers(integrated, ident.1=2, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST2, file.path("results", "markers_MAST", "cluster2-markers_MAST.csv"))
knitr::kable(head(markers_MAST2, 25))
| pre-rRNA:CR45847 |
0 |
-1.6052927 |
0.435 |
0.771 |
0 |
| CG13315 |
0 |
1.3570550 |
0.888 |
0.628 |
0 |
| pre-rRNA:CR45845 |
0 |
-1.3662956 |
0.546 |
0.838 |
0 |
| CG43349 |
0 |
1.3817990 |
0.643 |
0.296 |
0 |
| pre-rRNA:CR45846 |
0 |
-1.8422695 |
0.354 |
0.625 |
0 |
| MtnC |
0 |
0.9374882 |
0.911 |
0.673 |
0 |
| RpLP1 |
0 |
0.6907439 |
0.939 |
0.844 |
0 |
| RpS26 |
0 |
0.6451404 |
0.942 |
0.857 |
0 |
| Acbp5 |
0 |
1.3897104 |
0.870 |
0.672 |
0 |
| RpS21 |
0 |
0.7083381 |
0.885 |
0.773 |
0 |
| Cam |
0 |
0.9412521 |
0.872 |
0.708 |
0 |
| RpLP2 |
0 |
0.5753640 |
0.988 |
0.948 |
0 |
| CG34330 |
0 |
0.8335108 |
0.868 |
0.654 |
0 |
| 28SrRNA:CR45844 |
0 |
-1.1858404 |
0.391 |
0.629 |
0 |
| RpL41 |
0 |
0.6366086 |
0.990 |
0.972 |
0 |
| RpL37A |
0 |
0.6184733 |
0.965 |
0.914 |
0 |
| RpL19 |
0 |
0.6111552 |
0.950 |
0.862 |
0 |
| RpS30 |
0 |
0.6082303 |
0.935 |
0.857 |
0 |
| RpS27 |
0 |
0.5947775 |
0.954 |
0.885 |
0 |
| RpL13 |
0 |
0.6758232 |
0.871 |
0.746 |
0 |
| RpL35 |
0 |
0.6034513 |
0.965 |
0.889 |
0 |
| RpL23 |
0 |
0.5709788 |
0.965 |
0.920 |
0 |
| RpS28b |
0 |
0.6617437 |
0.903 |
0.800 |
0 |
| 18SrRNA:CR45838 |
0 |
-1.4476648 |
0.340 |
0.544 |
0 |
| 18SrRNA:CR41548 |
0 |
-1.4466951 |
0.340 |
0.544 |
0 |
Cluster 3
markers_MAST3 = FindMarkers(integrated, ident.1=3, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST3, file.path("results", "markers_MAST", "cluster3-markers_MAST.csv"))
knitr::kable(head(markers_MAST3, 25))
| lncRNA:CR40469 |
0 |
2.302270 |
0.999 |
0.907 |
0 |
| lncRNA:CR34335 |
0 |
2.055420 |
0.996 |
0.804 |
0 |
| Tet |
0 |
1.955220 |
0.607 |
0.167 |
0 |
| sty |
0 |
1.890713 |
0.616 |
0.261 |
0 |
| cib |
0 |
1.869054 |
0.716 |
0.303 |
0 |
| E(spl)mbeta-HLH |
0 |
1.832528 |
0.498 |
0.121 |
0 |
| Df31 |
0 |
1.714096 |
0.662 |
0.234 |
0 |
| E(spl)malpha-BFM |
0 |
1.699416 |
0.438 |
0.115 |
0 |
| hdc |
0 |
1.682541 |
0.539 |
0.210 |
0 |
| alphaTry |
0 |
-3.590280 |
0.302 |
0.877 |
0 |
| Jon65Aiv |
0 |
-3.274725 |
0.089 |
0.748 |
0 |
| esg |
0 |
1.623873 |
0.627 |
0.165 |
0 |
| betaTry |
0 |
-3.749718 |
0.116 |
0.765 |
0 |
| lncRNA:CR44898 |
0 |
1.181112 |
0.417 |
0.093 |
0 |
| MtnC |
0 |
-2.795806 |
0.108 |
0.738 |
0 |
| mew |
0 |
1.489866 |
0.524 |
0.196 |
0 |
| robo2 |
0 |
1.338593 |
0.374 |
0.084 |
0 |
| Vha16-1 |
0 |
-2.289525 |
0.243 |
0.837 |
0 |
| CG30025 |
0 |
-3.694229 |
0.121 |
0.724 |
0 |
| Jon99Ciii |
0 |
-3.770872 |
0.230 |
0.779 |
0 |
| Bace |
0 |
-3.387552 |
0.118 |
0.728 |
0 |
| CG13315 |
0 |
-2.205541 |
0.068 |
0.695 |
0 |
| N |
0 |
1.341854 |
0.367 |
0.102 |
0 |
| Jon99Cii |
0 |
-3.174153 |
0.155 |
0.699 |
0 |
| Jon65Aiii |
0 |
-2.990330 |
0.205 |
0.740 |
0 |
Cluster 4
markers_MAST4 = FindMarkers(integrated, ident.1=4, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST4, file.path("results", "markers_MAST", "cluster4-markers_MAST.csv"))
knitr::kable(head(markers_MAST4, 25))
| Mal-A7 |
0 |
2.5066108 |
0.846 |
0.268 |
0 |
| CG7542 |
0 |
1.9216820 |
0.917 |
0.277 |
0 |
| Bace |
0 |
1.6662209 |
1.000 |
0.657 |
0 |
| Diedel3 |
0 |
1.4456951 |
0.986 |
0.462 |
0 |
| CG5107 |
0 |
1.3861378 |
0.976 |
0.488 |
0 |
| epsilonTry |
0 |
1.3547922 |
0.967 |
0.434 |
0 |
| deltaTry |
0 |
1.3768449 |
0.974 |
0.504 |
0 |
| CG30031 |
0 |
1.3478686 |
0.972 |
0.507 |
0 |
| gammaTry |
0 |
1.3478686 |
0.972 |
0.507 |
0 |
| CG30025 |
0 |
1.3850072 |
1.000 |
0.652 |
0 |
| betaTry |
0 |
1.3489190 |
1.000 |
0.693 |
0 |
| CG8834 |
0 |
1.2595427 |
0.873 |
0.261 |
0 |
| Mal-A1 |
0 |
1.6094906 |
0.919 |
0.459 |
0 |
| Cyp4e1 |
0 |
1.5399858 |
0.788 |
0.314 |
0 |
| alphaTry |
0 |
1.2351138 |
1.000 |
0.820 |
0 |
| Pebp1 |
0 |
1.2475968 |
0.974 |
0.567 |
0 |
| CG4377 |
0 |
1.3900148 |
0.820 |
0.302 |
0 |
| Jon65Aiv |
0 |
1.0712409 |
0.992 |
0.674 |
0 |
| Mal-A8 |
0 |
1.4995523 |
0.663 |
0.198 |
0 |
| CG4363 |
0 |
1.3258361 |
0.683 |
0.231 |
0 |
| Jon65Aiii |
0 |
0.8930227 |
0.991 |
0.676 |
0 |
| tobi |
0 |
1.5496700 |
0.744 |
0.289 |
0 |
| MtnA |
0 |
-2.7459049 |
0.410 |
0.731 |
0 |
| LysD |
0 |
1.1315302 |
0.605 |
0.207 |
0 |
| CG12374 |
0 |
0.8567660 |
0.946 |
0.665 |
0 |
Cluster 5
markers_MAST5 = FindMarkers(integrated, ident.1=5, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST5, file.path("results", "markers_MAST", "cluster5-markers_MAST.csv"))
knitr::kable(head(markers_MAST5, 25))
| LManVI |
0 |
3.2826160 |
0.979 |
0.243 |
0 |
| LManV |
0 |
2.9089739 |
0.915 |
0.111 |
0 |
| ninaD |
0 |
2.6390118 |
0.871 |
0.126 |
0 |
| CG15534 |
0 |
2.2950064 |
0.852 |
0.135 |
0 |
| LManIII |
0 |
2.2069193 |
0.697 |
0.052 |
0 |
| LManII |
0 |
2.1743168 |
0.869 |
0.141 |
0 |
| CG31343 |
0 |
2.0399043 |
0.972 |
0.294 |
0 |
| Mal-A4 |
0 |
1.9131132 |
0.813 |
0.161 |
0 |
| iotaTry |
0 |
1.8477332 |
0.836 |
0.170 |
0 |
| LManI |
0 |
1.7075755 |
0.760 |
0.109 |
0 |
| CG7025 |
0 |
1.7006117 |
0.792 |
0.132 |
0 |
| Jon99Cii |
0 |
-2.6760089 |
0.608 |
0.660 |
0 |
| CG14629 |
0 |
1.4508569 |
0.741 |
0.135 |
0 |
| yip7 |
0 |
-2.6590889 |
0.577 |
0.544 |
0 |
| Jon65Aiv |
0 |
-2.9567791 |
0.605 |
0.704 |
0 |
| CG15533 |
0 |
1.4710121 |
0.688 |
0.063 |
0 |
| Pebp1 |
0 |
-2.5958549 |
0.586 |
0.598 |
0 |
| Cyp6d5 |
0 |
1.6516791 |
0.891 |
0.332 |
0 |
| Jon65Aiii |
0 |
-2.9732678 |
0.612 |
0.705 |
0 |
| lambdaTry |
0 |
1.5033115 |
0.862 |
0.217 |
0 |
| Npc2d |
0 |
1.7057162 |
0.684 |
0.113 |
0 |
| Rrp46 |
0 |
0.2793078 |
0.541 |
0.033 |
0 |
| CG11911 |
0 |
1.2775764 |
0.940 |
0.346 |
0 |
| asRNA:CR45281 |
0 |
1.1363389 |
0.746 |
0.158 |
0 |
| Jon99Ciii |
0 |
-3.2440617 |
0.649 |
0.743 |
0 |
Cluster 6
markers_MAST6 = FindMarkers(integrated, ident.1=6, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST6, file.path("results", "markers_MAST", "cluster6-markers_MAST.csv"))
knitr::kable(head(markers_MAST6, 25))
| CG43774 |
0 |
3.1529264 |
0.807 |
0.207 |
0 |
| MtnD |
0 |
2.6718753 |
0.775 |
0.307 |
0 |
| Arc1 |
0 |
2.4032135 |
0.911 |
0.374 |
0 |
| MtnC |
0 |
1.7872526 |
0.973 |
0.675 |
0 |
| CG15423 |
0 |
1.9412040 |
0.737 |
0.166 |
0 |
| mbl |
0 |
2.1404858 |
0.850 |
0.388 |
0 |
| thetaTry |
0 |
1.9437544 |
0.623 |
0.136 |
0 |
| CG34330 |
0 |
1.4639846 |
0.945 |
0.655 |
0 |
| CG5773 |
0 |
1.8438525 |
0.352 |
0.028 |
0 |
| CG5399 |
0 |
1.6595736 |
0.857 |
0.351 |
0 |
| CG34301 |
0 |
2.0971596 |
0.504 |
0.156 |
0 |
| Gagr |
0 |
0.5444069 |
0.415 |
0.024 |
0 |
| CG15422 |
0 |
1.7420240 |
0.730 |
0.245 |
0 |
| CG8177 |
0 |
2.2074678 |
0.574 |
0.251 |
0 |
| CG15127 |
0 |
1.7561668 |
0.354 |
0.076 |
0 |
| Stat92E |
0 |
1.7067355 |
0.631 |
0.212 |
0 |
| MtnB |
0 |
1.6650008 |
0.708 |
0.303 |
0 |
| CG5770 |
0 |
1.6160867 |
0.315 |
0.023 |
0 |
| MtnA |
0 |
1.4680532 |
0.959 |
0.693 |
0 |
| pre-rRNA:CR45847 |
0 |
-1.3870317 |
0.571 |
0.755 |
0 |
| MtnE |
0 |
1.6779141 |
0.642 |
0.249 |
0 |
| 28SrRNA:CR45844 |
0 |
-1.2991613 |
0.504 |
0.617 |
0 |
| Tsp42Ec |
0 |
1.5186541 |
0.562 |
0.187 |
0 |
| CG9672 |
0 |
1.7261596 |
0.306 |
0.112 |
0 |
| Adat1 |
0 |
1.0285194 |
0.640 |
0.249 |
0 |
Cluster 7
markers_MAST7 = FindMarkers(integrated, ident.1=7, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST7, file.path("results", "markers_MAST", "cluster7-markers_MAST.csv"))
knitr::kable(head(markers_MAST7, 25))
| Jon65Aii |
0 |
3.293820 |
0.814 |
0.210 |
0 |
| Jon99Fi |
0 |
3.266696 |
0.862 |
0.228 |
0 |
| CG7916 |
0 |
3.257496 |
0.750 |
0.252 |
0 |
| Jon25Biii |
0 |
3.256755 |
0.960 |
0.411 |
0 |
| Jon99Fii |
0 |
3.236508 |
0.940 |
0.255 |
0 |
| Jon65Ai |
0 |
2.885554 |
0.858 |
0.142 |
0 |
| CG7953 |
0 |
2.720265 |
0.800 |
0.203 |
0 |
| CG17571 |
0 |
2.704814 |
0.936 |
0.257 |
0 |
| CG10472 |
0 |
2.616301 |
1.000 |
0.405 |
0 |
| mag |
0 |
2.295322 |
0.929 |
0.172 |
0 |
| CG3106 |
0 |
1.981872 |
0.638 |
0.041 |
0 |
| CG8997 |
0 |
3.178884 |
0.801 |
0.263 |
0 |
| Ag5r2 |
0 |
1.913701 |
0.658 |
0.116 |
0 |
| CG11911 |
0 |
2.168190 |
0.933 |
0.348 |
0 |
| CG17633 |
0 |
2.406228 |
0.811 |
0.222 |
0 |
| PGRP-SC1b |
0 |
0.844151 |
0.454 |
0.007 |
0 |
| CG16749 |
0 |
1.553809 |
0.894 |
0.276 |
0 |
| Acbp5 |
0 |
1.379220 |
0.998 |
0.670 |
0 |
| CG8093 |
0 |
1.531794 |
0.670 |
0.092 |
0 |
| Jon66Ci |
0 |
2.028641 |
0.552 |
0.041 |
0 |
| Jon44E |
0 |
2.093519 |
0.517 |
0.065 |
0 |
| CG3868 |
0 |
1.400003 |
0.902 |
0.330 |
0 |
| Bace |
0 |
-3.270406 |
0.466 |
0.694 |
0 |
| CG31198 |
0 |
1.287501 |
0.885 |
0.290 |
0 |
| asRNA:CR45281 |
0 |
1.095471 |
0.709 |
0.161 |
0 |
Cluster 8
markers_MAST8 = FindMarkers(integrated, ident.1=8, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST8, file.path("results", "markers_MAST", "cluster8-markers_MAST.csv"))
knitr::kable(head(markers_MAST8, 25))
| CG5767 |
0 |
4.1823644 |
0.949 |
0.170 |
0 |
| MtnB |
0 |
3.4244227 |
0.947 |
0.297 |
0 |
| CG30479 |
0 |
3.3674916 |
0.898 |
0.135 |
0 |
| CG6277 |
0 |
3.3655225 |
0.646 |
0.112 |
0 |
| CG8661 |
0 |
3.3205974 |
0.757 |
0.145 |
0 |
| Vha100-4 |
0 |
2.9384083 |
0.664 |
0.047 |
0 |
| CG31446 |
0 |
2.6010173 |
0.653 |
0.064 |
0 |
| CG15423 |
0 |
2.5263314 |
0.895 |
0.165 |
0 |
| Tsp42Ec |
0 |
2.5020736 |
0.833 |
0.179 |
0 |
| Adat1 |
0 |
2.4189735 |
0.889 |
0.242 |
0 |
| Vha16-1 |
0 |
2.3347834 |
1.000 |
0.783 |
0 |
| CG30480 |
0 |
2.2971498 |
0.802 |
0.056 |
0 |
| Vha13 |
0 |
1.8668964 |
0.996 |
0.667 |
0 |
| CAH1 |
0 |
2.2784895 |
0.777 |
0.132 |
0 |
| CG17109 |
0 |
2.0909091 |
0.644 |
0.130 |
0 |
| Vha55 |
0 |
2.3064912 |
0.958 |
0.416 |
0 |
| CG5399 |
0 |
2.1605505 |
0.913 |
0.354 |
0 |
| CG11192 |
0 |
0.3031397 |
0.474 |
0.005 |
0 |
| CG31087 |
0 |
2.0721848 |
0.751 |
0.208 |
0 |
| Vha14-1 |
0 |
1.9189858 |
0.900 |
0.299 |
0 |
| Vha44 |
0 |
2.2445595 |
0.964 |
0.474 |
0 |
| Vha68-2 |
0 |
2.1241612 |
0.931 |
0.341 |
0 |
| CG15422 |
0 |
1.9328725 |
0.884 |
0.243 |
0 |
| VhaAC39-1 |
0 |
2.2447769 |
0.913 |
0.328 |
0 |
| CG6271 |
0 |
1.1021336 |
0.428 |
0.005 |
0 |
Cluster 9
markers_MAST9 = FindMarkers(integrated, ident.1=9, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST9, file.path("results", "markers_MAST", "cluster9-markers_MAST.csv"))
knitr::kable(head(markers_MAST9, 25))
| Amy-p |
0 |
3.7546156 |
0.926 |
0.413 |
0 |
| CG6839 |
0 |
3.6581995 |
0.622 |
0.156 |
0 |
| CG3819 |
0 |
3.3652770 |
0.657 |
0.126 |
0 |
| CG11400 |
0 |
3.3465943 |
0.688 |
0.146 |
0 |
| CG4928 |
0 |
3.3092892 |
0.678 |
0.197 |
0 |
| CG14125 |
0 |
4.9471279 |
0.563 |
0.197 |
0 |
| CG15818 |
0 |
3.2587424 |
0.492 |
0.108 |
0 |
| Amy-d |
0 |
3.4545206 |
0.718 |
0.233 |
0 |
| CG15043 |
0 |
3.0710478 |
0.642 |
0.159 |
0 |
| Mco1 |
0 |
2.4300353 |
0.424 |
0.061 |
0 |
| CG13324 |
0 |
2.1388592 |
0.355 |
0.133 |
0 |
| CG7589 |
0 |
2.2166688 |
0.386 |
0.075 |
0 |
| Ag5r |
0 |
2.4361222 |
0.307 |
0.066 |
0 |
| CG13323 |
0 |
2.4128346 |
0.612 |
0.282 |
0 |
| Adhr |
0 |
1.6133663 |
0.541 |
0.202 |
0 |
| CG42486 |
0 |
1.0222549 |
0.310 |
0.015 |
0 |
| CG14933 |
0 |
1.4466203 |
0.368 |
0.045 |
0 |
| CG18223 |
0 |
0.6894215 |
0.231 |
0.006 |
0 |
| Adh |
0 |
1.5147031 |
0.503 |
0.189 |
0 |
| CG10405 |
0 |
1.0750603 |
0.246 |
0.027 |
0 |
| lncRNA:CR40469 |
0 |
0.6702035 |
0.995 |
0.911 |
0 |
| CG14439 |
0 |
0.7704120 |
0.241 |
0.010 |
0 |
| CG42729 |
0 |
0.2725671 |
0.165 |
0.001 |
0 |
| isoQC |
0 |
1.4823857 |
0.358 |
0.228 |
0 |
| CG9701 |
0 |
0.5324651 |
0.208 |
0.007 |
0 |
Cluster 10
markers_MAST10 = FindMarkers(integrated, ident.1=10, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST10, file.path("results", "markers_MAST", "cluster10-markers_MAST.csv"))
knitr::kable(head(markers_MAST10, 25))
| 18SrRNA-Psi:CR45861 |
0 |
1.9929792 |
0.774 |
0.425 |
0 |
| 18SrRNA:CR41548 |
0 |
1.8092135 |
0.917 |
0.516 |
0 |
| 18SrRNA:CR45838 |
0 |
1.8088839 |
0.917 |
0.516 |
0 |
| 18SrRNA:CR45841 |
0 |
1.8323186 |
0.902 |
0.506 |
0 |
| pre-rRNA:CR45846 |
0 |
1.8543423 |
0.967 |
0.592 |
0 |
| 18SrRNA-Psi:CR41602 |
0 |
1.9522780 |
0.714 |
0.415 |
0 |
| pre-rRNA:CR45856 |
0 |
1.9342579 |
0.842 |
0.497 |
0 |
| pre-rRNA:CR45847 |
0 |
1.6122745 |
0.994 |
0.737 |
0 |
| pre-rRNA:CR45845 |
0 |
1.5562434 |
0.979 |
0.810 |
0 |
| 28SrRNA-Psi:CR45851 |
0 |
1.3256885 |
0.688 |
0.531 |
0 |
| 28SrRNA-Psi:CR41609 |
0 |
1.3964064 |
0.560 |
0.459 |
0 |
| 28SrRNA:CR45844 |
0 |
1.1993126 |
0.744 |
0.607 |
0 |
| 28SrRNA-Psi:CR40741 |
0 |
1.3577011 |
0.565 |
0.448 |
0 |
| 28SrRNA:CR45837 |
0 |
0.7395038 |
0.411 |
0.486 |
0 |
| CG12374 |
0 |
0.3401385 |
0.943 |
0.678 |
0 |
| 28SrRNA-Psi:CR45855 |
0 |
0.4141037 |
0.098 |
0.243 |
0 |
| 14-3-3epsilon |
0 |
-0.6633088 |
0.158 |
0.488 |
0 |
| Jon99Cii |
0 |
0.5646288 |
0.893 |
0.650 |
0 |
| Hr4 |
0 |
0.5679335 |
0.321 |
0.432 |
0 |
| rg |
0 |
0.3604389 |
0.137 |
0.328 |
0 |
| Bace |
0 |
0.2581049 |
0.923 |
0.674 |
0 |
| His2Av |
0 |
-0.3896197 |
0.062 |
0.330 |
0 |
| Jon99Ciii |
0 |
0.2742590 |
0.938 |
0.731 |
0 |
| CG30025 |
0 |
0.2913271 |
0.899 |
0.671 |
0 |
| CG42322 |
0 |
-0.2620372 |
0.182 |
0.471 |
0 |
Cluster 11
markers_MAST11 = FindMarkers(integrated, ident.1=11, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST11, file.path("results", "markers_MAST", "cluster11-markers_MAST.csv"))
knitr::kable(head(markers_MAST11, 25))
| Acbp3 |
0 |
2.7989831 |
0.978 |
0.298 |
0 |
| CG32473 |
0 |
2.6193899 |
0.739 |
0.143 |
0 |
| Gs2 |
0 |
2.5571275 |
0.978 |
0.369 |
0 |
| CG15254 |
0 |
2.5365965 |
0.796 |
0.138 |
0 |
| zetaTry |
0 |
2.2494411 |
0.873 |
0.180 |
0 |
| CG31266 |
0 |
2.3784007 |
0.672 |
0.081 |
0 |
| NAAT1 |
0 |
1.9975076 |
0.669 |
0.058 |
0 |
| CG8774 |
0 |
2.0324823 |
0.768 |
0.148 |
0 |
| CG13492 |
0 |
1.8982504 |
0.876 |
0.217 |
0 |
| CG31343 |
0 |
1.8074765 |
0.984 |
0.310 |
0 |
| CG4053 |
0 |
1.8113458 |
0.573 |
0.030 |
0 |
| Pdk |
0 |
1.7664866 |
0.640 |
0.155 |
0 |
| CG31267 |
0 |
2.2780699 |
0.564 |
0.030 |
0 |
| CG31265 |
0 |
1.8975320 |
0.580 |
0.052 |
0 |
| Gdh |
0 |
1.7188263 |
0.768 |
0.192 |
0 |
| Pepck2 |
0 |
1.7380777 |
0.653 |
0.096 |
0 |
| CG9673 |
0 |
1.7049865 |
0.895 |
0.234 |
0 |
| CG31269 |
0 |
1.3435526 |
0.433 |
0.007 |
0 |
| CG31091 |
0 |
1.9780214 |
0.503 |
0.031 |
0 |
| CG17475 |
0 |
1.9069711 |
0.411 |
0.005 |
0 |
| CG32379 |
0 |
1.2844718 |
0.427 |
0.008 |
0 |
| CG5958 |
0 |
1.7785181 |
0.726 |
0.177 |
0 |
| CBP |
0 |
0.2538569 |
0.344 |
0.001 |
0 |
| Agpat4 |
0 |
1.5228590 |
0.761 |
0.194 |
0 |
| CG10116 |
0 |
1.7645785 |
0.812 |
0.219 |
0 |
Cluster 12
markers_MAST12 = FindMarkers(integrated, ident.1=12, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST12, file.path("results", "markers_MAST", "cluster12-markers_MAST.csv"))
knitr::kable(head(markers_MAST12, 25))
| CCHa1 |
0 |
2.954678 |
0.603 |
0.119 |
0 |
| IA-2 |
0 |
2.647187 |
0.993 |
0.403 |
0 |
| nrv3 |
0 |
2.247964 |
0.909 |
0.242 |
0 |
| Npc2b |
0 |
2.621671 |
0.593 |
0.121 |
0 |
| CG42541 |
0 |
2.090760 |
0.547 |
0.137 |
0 |
| 7B2 |
0 |
2.096422 |
0.915 |
0.231 |
0 |
| w |
0 |
2.291362 |
0.759 |
0.206 |
0 |
| CCHa2 |
0 |
4.295649 |
0.407 |
0.249 |
0 |
| Galphao |
0 |
1.910813 |
0.691 |
0.175 |
0 |
| pros |
0 |
2.015971 |
0.769 |
0.181 |
0 |
| Pal2 |
0 |
2.079188 |
0.782 |
0.147 |
0 |
| CG42613 |
0 |
1.576964 |
0.450 |
0.043 |
0 |
| CG30183 |
0 |
1.964119 |
0.736 |
0.130 |
0 |
| Phm |
0 |
1.793405 |
0.866 |
0.234 |
0 |
| heph |
0 |
1.849458 |
0.664 |
0.104 |
0 |
| CG17646 |
0 |
1.608020 |
0.453 |
0.094 |
0 |
| CG7191 |
0 |
2.818244 |
0.332 |
0.074 |
0 |
| Tk |
0 |
3.468051 |
0.557 |
0.218 |
0 |
| Mrp4 |
0 |
1.856973 |
0.528 |
0.050 |
0 |
| AstC |
0 |
3.204312 |
0.524 |
0.302 |
0 |
| amon |
0 |
1.943279 |
0.638 |
0.127 |
0 |
| Nrx-1 |
0 |
1.644528 |
0.570 |
0.095 |
0 |
| CG32547 |
0 |
1.679896 |
0.463 |
0.056 |
0 |
| unc-13-4A |
0 |
2.278254 |
0.632 |
0.172 |
0 |
| Hsp22 |
0 |
1.719189 |
0.720 |
0.342 |
0 |
Cluster 13
markers_MAST13 = FindMarkers(integrated, ident.1=13, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST13, file.path("results", "markers_MAST", "cluster13-markers_MAST.csv"))
knitr::kable(head(markers_MAST13, 25))
| CG42825 |
0 |
2.2269327 |
0.761 |
0.116 |
0 |
| CG14499 |
0 |
3.0172332 |
0.764 |
0.075 |
0 |
| lectin-37Da |
0 |
2.7082242 |
0.757 |
0.081 |
0 |
| Mur29B |
0 |
2.5662759 |
0.804 |
0.184 |
0 |
| CG33926 |
0 |
2.1060157 |
0.950 |
0.228 |
0 |
| CG10912 |
0 |
2.2182013 |
0.907 |
0.203 |
0 |
| CG32368 |
0 |
2.1692952 |
0.854 |
0.168 |
0 |
| asRNA:CR44192 |
0 |
1.3426450 |
0.635 |
0.150 |
0 |
| Npc2e |
0 |
2.1977119 |
0.691 |
0.154 |
0 |
| CG10911 |
0 |
1.5862509 |
0.963 |
0.401 |
0 |
| CG31086 |
0 |
1.5386260 |
0.804 |
0.311 |
0 |
| CG8353 |
0 |
1.6389178 |
0.535 |
0.155 |
0 |
| CG31323 |
0 |
1.7430702 |
0.837 |
0.335 |
0 |
| CG11912 |
0 |
2.4572814 |
0.412 |
0.086 |
0 |
| CG10943 |
0 |
1.3910626 |
0.498 |
0.151 |
0 |
| CG18493 |
0 |
1.4654841 |
0.754 |
0.253 |
0 |
| CG9568 |
0 |
1.6351462 |
0.688 |
0.224 |
0 |
| CG15255 |
0 |
1.3555371 |
0.771 |
0.267 |
0 |
| lectin-37Db |
0 |
1.3335771 |
0.688 |
0.226 |
0 |
| RpL3 |
0 |
-0.9937845 |
0.718 |
0.896 |
0 |
| CG13482 |
0 |
1.4957212 |
0.625 |
0.217 |
0 |
| Cht4 |
0 |
0.7473134 |
0.309 |
0.016 |
0 |
| Nazo |
0 |
1.1559679 |
0.518 |
0.128 |
0 |
| CG8950 |
0 |
0.3780558 |
0.455 |
0.065 |
0 |
| CG14500 |
0 |
0.8257770 |
0.518 |
0.090 |
0 |
Cluster 14
markers_MAST14 = FindMarkers(integrated, ident.1=14, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST14, file.path("results", "markers_MAST", "cluster14-markers_MAST.csv"))
knitr::kable(head(markers_MAST14, 25))
| NPF |
0 |
5.1479016 |
0.983 |
0.290 |
0 |
| chrb |
0 |
3.5003289 |
0.841 |
0.281 |
0 |
| IA-2 |
0 |
3.2135054 |
1.000 |
0.403 |
0 |
| 7B2 |
0 |
3.0448420 |
0.924 |
0.231 |
0 |
| Phm |
0 |
2.6654247 |
0.890 |
0.234 |
0 |
| Tk |
0 |
2.4481634 |
0.857 |
0.210 |
0 |
| svr |
0 |
2.5674166 |
0.867 |
0.243 |
0 |
| Ih |
0 |
2.4344804 |
0.661 |
0.097 |
0 |
| CG15312 |
0 |
1.9534549 |
0.591 |
0.071 |
0 |
| CG30183 |
0 |
2.1819966 |
0.764 |
0.129 |
0 |
| nrv3 |
0 |
2.2510264 |
0.827 |
0.245 |
0 |
| cpo |
0 |
2.4463486 |
0.761 |
0.275 |
0 |
| Hsp23 |
0 |
2.8419301 |
0.781 |
0.318 |
0 |
| esg |
0 |
2.2221630 |
0.761 |
0.184 |
0 |
| Pal2 |
0 |
2.1567229 |
0.748 |
0.149 |
0 |
| Hsp22 |
0 |
1.8522887 |
0.777 |
0.341 |
0 |
| Sh |
0 |
1.6991290 |
0.492 |
0.042 |
0 |
| Hk |
0 |
1.8939359 |
0.495 |
0.026 |
0 |
| Ldh |
0 |
2.2306744 |
0.728 |
0.177 |
0 |
| CG46385 |
0 |
2.2108067 |
0.831 |
0.325 |
0 |
| unc-13-4A |
0 |
1.7157612 |
0.757 |
0.169 |
0 |
| AstA-R2 |
0 |
1.3280768 |
0.425 |
0.014 |
0 |
| Hsp70Aa |
0 |
1.7015774 |
0.724 |
0.352 |
0 |
| Or49b |
0 |
0.8860271 |
0.342 |
0.002 |
0 |
| Hsp70Ab |
0 |
1.6730323 |
0.714 |
0.350 |
0 |
Cluster 15
markers_MAST15 = FindMarkers(integrated, ident.1=15, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST15, file.path("results", "markers_MAST", "cluster15-markers_MAST.csv"))
knitr::kable(head(markers_MAST15, 25))
| Orcokinin |
0 |
5.3271710 |
1.000 |
0.292 |
0 |
| AstC |
0 |
3.3331658 |
0.996 |
0.293 |
0 |
| CG4587 |
0 |
2.9631960 |
0.743 |
0.008 |
0 |
| CG14989 |
0 |
3.7994074 |
0.871 |
0.190 |
0 |
| CG13135 |
0 |
0.5614222 |
0.680 |
0.002 |
0 |
| svr |
0 |
3.0093252 |
0.979 |
0.244 |
0 |
| CG33639 |
0 |
0.2836585 |
0.676 |
0.002 |
0 |
| tap |
0 |
1.9801462 |
0.851 |
0.030 |
0 |
| RYa-R |
0 |
0.3823188 |
0.593 |
0.000 |
0 |
| unc-13-4A |
0 |
2.9465008 |
0.963 |
0.167 |
0 |
| Pvf3 |
0 |
0.3481559 |
0.676 |
0.005 |
0 |
| CG30340 |
0 |
0.8985034 |
0.614 |
0.002 |
0 |
| blot |
0 |
0.6021470 |
0.680 |
0.009 |
0 |
| Rgk3 |
0 |
2.1701331 |
0.768 |
0.022 |
0 |
| Gad1 |
0 |
0.2640746 |
0.573 |
0.001 |
0 |
| CG30116 |
0 |
1.2765806 |
0.714 |
0.013 |
0 |
| dac |
0 |
1.2441220 |
0.710 |
0.013 |
0 |
| CG34458 |
0 |
0.7437293 |
0.685 |
0.010 |
0 |
| Slob |
0 |
0.7153288 |
0.685 |
0.011 |
0 |
| brp |
0 |
1.3592246 |
0.739 |
0.021 |
0 |
| CG15203 |
0 |
0.8878283 |
0.544 |
0.001 |
0 |
| CG15894 |
0 |
0.3864234 |
0.676 |
0.011 |
0 |
| Frq1 |
0 |
0.5909662 |
0.614 |
0.009 |
0 |
| CG3955 |
0 |
0.3744050 |
0.660 |
0.011 |
0 |
| CG4341 |
0 |
0.5783358 |
0.685 |
0.014 |
0 |
Cluster 16
markers_MAST16 = FindMarkers(integrated, ident.1=16, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST16, file.path("results", "markers_MAST", "cluster16-markers_MAST.csv"))
knitr::kable(head(markers_MAST16, 25))
| EbpIII |
0 |
4.834704 |
0.920 |
0.223 |
0 |
| spidey |
0 |
3.329530 |
0.734 |
0.216 |
0 |
| CG31522 |
0 |
3.284502 |
0.688 |
0.226 |
0 |
| to |
0 |
3.587573 |
0.726 |
0.064 |
0 |
| Adhr |
0 |
3.095023 |
0.819 |
0.200 |
0 |
| Adh |
0 |
3.083127 |
0.776 |
0.187 |
0 |
| CG1124 |
0 |
3.623984 |
0.620 |
0.110 |
0 |
| CG30197 |
0 |
3.532219 |
0.684 |
0.094 |
0 |
| emp |
0 |
2.438345 |
0.523 |
0.089 |
0 |
| wat |
0 |
3.231272 |
0.532 |
0.046 |
0 |
| CG10237 |
0 |
2.613266 |
0.536 |
0.048 |
0 |
| ple |
0 |
2.881654 |
0.553 |
0.030 |
0 |
| Msr-110 |
0 |
2.666640 |
0.612 |
0.116 |
0 |
| CG31523 |
0 |
2.969051 |
0.658 |
0.148 |
0 |
| ADPS |
0 |
2.858588 |
0.473 |
0.050 |
0 |
| Acbp2 |
0 |
2.549511 |
0.903 |
0.364 |
0 |
| vir-1 |
0 |
2.554232 |
0.494 |
0.033 |
0 |
| CG3097 |
0 |
2.313336 |
0.485 |
0.065 |
0 |
| ATPCL |
0 |
2.223677 |
0.527 |
0.170 |
0 |
| Men |
0 |
2.158755 |
0.515 |
0.191 |
0 |
| CG8306 |
0 |
2.509803 |
0.494 |
0.060 |
0 |
| CG43134 |
0 |
4.113872 |
0.354 |
0.116 |
0 |
| Ace |
0 |
1.772902 |
0.430 |
0.135 |
0 |
| Cpr67B |
0 |
2.731262 |
0.435 |
0.042 |
0 |
| CG4660 |
0 |
2.353659 |
0.376 |
0.042 |
0 |
Cluster 17
markers_MAST17 = FindMarkers(integrated, ident.1=17, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST17, file.path("results", "markers_MAST", "cluster17-markers_MAST.csv"))
knitr::kable(head(markers_MAST17, 25))
| Skp2 |
0 |
5.3000419 |
1.000 |
0.389 |
0 |
| CG34324 |
0 |
5.2816359 |
0.987 |
0.277 |
0 |
| CG14645 |
0 |
5.1663525 |
1.000 |
0.282 |
0 |
| CG34220 |
0 |
5.0936717 |
0.987 |
0.420 |
0 |
| CG3906 |
0 |
5.0871549 |
0.991 |
0.217 |
0 |
| Muc68D |
0 |
5.0305282 |
0.933 |
0.207 |
0 |
| CG11672 |
0 |
3.8428054 |
0.871 |
0.037 |
0 |
| CG4783 |
0 |
3.2040502 |
0.956 |
0.160 |
0 |
| Pgant4 |
0 |
2.4506775 |
0.662 |
0.014 |
0 |
| Idgf4 |
0 |
2.4551618 |
0.782 |
0.072 |
0 |
| CG30026 |
0 |
1.7591050 |
0.511 |
0.003 |
0 |
| CG43673 |
0 |
2.9267167 |
0.467 |
0.020 |
0 |
| opm |
0 |
1.6150495 |
0.462 |
0.147 |
0 |
| CG9988 |
0 |
1.2403834 |
0.351 |
0.001 |
0 |
| CG31077 |
0 |
2.0244394 |
0.298 |
0.002 |
0 |
| sgl |
0 |
1.4856293 |
0.387 |
0.187 |
0 |
| Cys |
0 |
1.5106883 |
0.507 |
0.172 |
0 |
| Pgant8 |
0 |
0.8396921 |
0.271 |
0.002 |
0 |
| CG13810 |
0 |
0.9988078 |
0.329 |
0.008 |
0 |
| CG45061 |
0 |
1.3525408 |
0.342 |
0.026 |
0 |
| Gfat1 |
0 |
1.3953112 |
0.360 |
0.097 |
0 |
| NijC |
0 |
1.1420618 |
0.271 |
0.018 |
0 |
| Ctl2 |
0 |
1.3710576 |
0.387 |
0.104 |
0 |
| Hexo1 |
0 |
1.4742258 |
0.489 |
0.142 |
0 |
| CG30025 |
0 |
-3.1632394 |
0.116 |
0.690 |
0 |
Cluster 18
markers_MAST18 = FindMarkers(integrated, ident.1=18, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST18, file.path("results", "markers_MAST", "cluster18-markers_MAST.csv"))
knitr::kable(head(markers_MAST18, 25))
| Npc2f |
0 |
3.6101816 |
0.988 |
0.150 |
0 |
| thetaTry |
0 |
3.0983381 |
0.988 |
0.149 |
0 |
| CG4830 |
0 |
2.6994819 |
0.789 |
0.017 |
0 |
| CG18404 |
0 |
4.0870292 |
0.845 |
0.161 |
0 |
| CG4563 |
0 |
2.4116003 |
0.770 |
0.061 |
0 |
| Peritrophin-15a |
0 |
2.6028515 |
0.950 |
0.184 |
0 |
| CG9682 |
0 |
2.5655588 |
0.727 |
0.027 |
0 |
| yip7 |
0 |
2.3389780 |
1.000 |
0.539 |
0 |
| Jon65Aiv |
0 |
2.1546865 |
1.000 |
0.694 |
0 |
| Jon65Aiii |
0 |
2.0314714 |
1.000 |
0.695 |
0 |
| CG4734 |
0 |
1.8411515 |
0.826 |
0.132 |
0 |
| CG30031 |
0 |
1.3828164 |
1.000 |
0.535 |
0 |
| gammaTry |
0 |
1.3828164 |
1.000 |
0.535 |
0 |
| Bace |
0 |
1.8774625 |
0.994 |
0.678 |
0 |
| deltaTry |
0 |
1.3582514 |
1.000 |
0.532 |
0 |
| CG34040 |
0 |
0.5422801 |
0.640 |
0.082 |
0 |
| LysB |
0 |
1.3202083 |
0.826 |
0.197 |
0 |
| 28SrRNA-Psi:CR45853 |
0 |
0.3463674 |
0.422 |
0.025 |
0 |
| Pebp1 |
0 |
1.5259964 |
0.944 |
0.592 |
0 |
| CG10477 |
0 |
1.4562131 |
0.665 |
0.131 |
0 |
| Pgcl |
0 |
0.4049199 |
0.553 |
0.069 |
0 |
| lncRNA:CR34335 |
0 |
-2.0779373 |
0.652 |
0.821 |
0 |
| LysD |
0 |
1.1953521 |
0.776 |
0.228 |
0 |
| CG30025 |
0 |
1.0353096 |
1.000 |
0.673 |
0 |
| LManII |
0 |
0.9659594 |
0.708 |
0.172 |
0 |
Cluster 19
markers_MAST19 = FindMarkers(integrated, ident.1=19, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST19, file.path("results", "markers_MAST", "cluster19-markers_MAST.csv"))
knitr::kable(head(markers_MAST19, 25))
| CG6409 |
0 |
4.803699 |
0.966 |
0.145 |
0 |
| CG42656 |
0 |
4.436801 |
0.910 |
0.082 |
0 |
| IM33 |
0 |
3.965089 |
0.831 |
0.081 |
0 |
| CG1143 |
0 |
2.846481 |
0.584 |
0.052 |
0 |
| Irk2 |
0 |
2.881903 |
0.584 |
0.057 |
0 |
| Ggamma30A |
0 |
2.425736 |
0.584 |
0.061 |
0 |
| tws |
0 |
2.662732 |
0.640 |
0.170 |
0 |
| CG14949 |
0 |
1.958402 |
0.315 |
0.027 |
0 |
| CG16704 |
0 |
2.138345 |
0.360 |
0.030 |
0 |
| Nha2 |
0 |
2.023512 |
0.382 |
0.042 |
0 |
| Gdh |
0 |
2.581828 |
0.685 |
0.205 |
0 |
| CG9993 |
0 |
1.838703 |
0.371 |
0.012 |
0 |
| Vha13 |
0 |
1.570255 |
0.978 |
0.679 |
0 |
| CG18473 |
0 |
1.749039 |
0.337 |
0.018 |
0 |
| Msr-110 |
0 |
2.274386 |
0.596 |
0.123 |
0 |
| Gdap1 |
0 |
1.572230 |
0.270 |
0.149 |
0 |
| Nop17l |
0 |
1.449145 |
0.326 |
0.154 |
0 |
| GLS |
0 |
1.632297 |
0.180 |
0.046 |
0 |
| lncRNA:CR40469 |
0 |
1.381869 |
1.000 |
0.913 |
0 |
| bru1 |
0 |
1.433266 |
0.247 |
0.014 |
0 |
| CG14933 |
0 |
1.864400 |
0.461 |
0.054 |
0 |
| CG17999 |
0 |
1.509553 |
0.303 |
0.011 |
0 |
| sesB |
0 |
1.222895 |
0.978 |
0.775 |
0 |
| Vha16-1 |
0 |
1.169516 |
0.978 |
0.791 |
0 |
| CG7365 |
0 |
1.059547 |
0.225 |
0.015 |
0 |
Cluster 20
markers_MAST20 = FindMarkers(integrated, ident.1=20, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST20, file.path("results", "markers_MAST", "cluster20-markers_MAST.csv"))
knitr::kable(head(markers_MAST20, 25))
| CG13285 |
0 |
3.8504561 |
0.963 |
0.020 |
0 |
| CG44013 |
0 |
2.9005776 |
0.778 |
0.020 |
0 |
| Msr-110 |
0 |
3.1788920 |
0.963 |
0.123 |
0 |
| Idgf6 |
0 |
2.6238014 |
0.778 |
0.043 |
0 |
| CG43394 |
0 |
2.6414936 |
0.685 |
0.028 |
0 |
| GstD11 |
0 |
2.0589475 |
0.537 |
0.001 |
0 |
| CG10096 |
0 |
2.8104611 |
0.667 |
0.012 |
0 |
| CG5065 |
0 |
2.2587751 |
0.741 |
0.095 |
0 |
| Idgf4 |
0 |
2.3408393 |
0.815 |
0.083 |
0 |
| ELOVL |
0 |
1.5613344 |
0.370 |
0.087 |
0 |
| bond |
0 |
2.0227798 |
0.481 |
0.016 |
0 |
| Skp2 |
0 |
2.3129974 |
0.593 |
0.401 |
0 |
| CG9336 |
0 |
1.7430299 |
0.500 |
0.041 |
0 |
| CG17839 |
0 |
1.6639478 |
0.426 |
0.030 |
0 |
| CG34220 |
0 |
2.2185041 |
0.556 |
0.432 |
0 |
| yellow-d |
0 |
1.4443748 |
0.333 |
0.002 |
0 |
| Cys |
0 |
1.8452262 |
0.611 |
0.177 |
0 |
| trn |
0 |
1.5692802 |
0.407 |
0.090 |
0 |
| Gasp |
0 |
1.5855129 |
0.389 |
0.008 |
0 |
| Muc68D |
0 |
1.8044905 |
0.519 |
0.221 |
0 |
| pyr |
0 |
1.1379466 |
0.296 |
0.205 |
0 |
| CG34324 |
0 |
1.9788417 |
0.500 |
0.291 |
0 |
| Baldspot |
0 |
1.9471257 |
0.537 |
0.252 |
0 |
| Snp |
0 |
0.9650331 |
0.167 |
0.131 |
0 |
| CG14645 |
0 |
2.2891081 |
0.593 |
0.296 |
0 |
Cluster 21
markers_MAST21 = FindMarkers(integrated, ident.1=21, test.use="MAST") %>%
as.data.frame() %>%
tibble::rownames_to_column("gene") %>%
arrange(p_val_adj)
readr::write_csv(markers_MAST21, file.path("results", "markers_MAST", "cluster21-markers_MAST.csv"))
knitr::kable(head(markers_MAST21, 25))
| Mur18B |
0 |
3.763457 |
0.583 |
0.080 |
0 |
| Alp2 |
0 |
3.229087 |
0.556 |
0.029 |
0 |
| CG6726 |
0 |
3.195073 |
0.611 |
0.109 |
0 |
| Alp4 |
0 |
4.009171 |
0.639 |
0.055 |
0 |
| CG3168 |
0 |
3.775266 |
0.528 |
0.073 |
0 |
| Spat |
0 |
1.457579 |
0.278 |
0.026 |
0 |
| Oatp58Dc |
0 |
1.726650 |
0.222 |
0.015 |
0 |
| CG2680 |
0 |
1.974845 |
0.361 |
0.026 |
0 |
| CG10513 |
0 |
2.711481 |
0.361 |
0.037 |
0 |
| CG7882 |
0 |
2.203185 |
0.167 |
0.016 |
0 |
| CG10226 |
0 |
1.227024 |
0.250 |
0.032 |
0 |
| Smvt |
0 |
2.016667 |
0.306 |
0.015 |
0 |
| CG10553 |
0 |
1.840484 |
0.333 |
0.016 |
0 |
| CG7084 |
0 |
2.335300 |
0.361 |
0.091 |
0 |
| CG6733 |
0 |
1.778015 |
0.306 |
0.028 |
0 |
| CG10560 |
0 |
1.147329 |
0.139 |
0.020 |
0 |
| Irk3 |
0 |
1.842469 |
0.333 |
0.018 |
0 |
| CG31373 |
0 |
1.888380 |
0.333 |
0.112 |
0 |
| CG10514 |
0 |
2.743189 |
0.333 |
0.009 |
0 |
| Tps1 |
0 |
1.900322 |
0.222 |
0.009 |
0 |
| HDAC6 |
0 |
2.516269 |
0.500 |
0.161 |
0 |
| UGP |
0 |
2.228555 |
0.500 |
0.160 |
0 |
| CG13309 |
0 |
2.391852 |
0.333 |
0.008 |
0 |
| CG14292 |
0 |
3.042500 |
0.417 |
0.098 |
0 |
| CG11426 |
0 |
1.755369 |
0.306 |
0.109 |
0 |
saveRDS(integrated, file.path("results","integrated.rds"))